id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
dcef246a637f522e9a4fbfd246680720bd7c0af01f2add9690881a649be12c15 | [
"arxiv",
"semantic_scholar"
] | Federated Learning With Individualized Privacy Through Client Sampling | With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy setti... | [
"Lucas Lange",
"Ole Borchardt",
"Erhard Rahm"
] | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2025-01-29T00:00:00 | https://arxiv.org/abs/2501.17634 | https://arxiv.org/pdf/2501.17634v2 | 2501.17634 | 10.1109/ICMLT65785.2025.11193238 | 2 | 0 | false | null | International Conference on Machine Learning Technologies | 0.1193 |
df0476d2362e01c4c0fcb286f5a6dacfb926d89ed5da60f0f81ebc0892129bf3 | [
"arxiv",
"semantic_scholar"
] | Optimal Strategies for Federated Learning Maintaining Client Privacy | Federated Learning (FL) emerged as a learning method to enable the server to train models over data distributed among various clients. These clients are protective about their data being leaked to the server, any other client, or an external adversary, and hence, locally train the model and share it with the server rat... | [
"Uday Bhaskar",
"Varul Srivastava",
"Avyukta Manjunatha Vummintala",
"Naresh Manwani",
"Sujit Gujar"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-01-24T00:00:00 | https://arxiv.org/abs/2501.14453 | https://arxiv.org/pdf/2501.14453v1 | 2501.14453 | 10.48550/arXiv.2501.14453 | 0 | 0 | false | null | arXiv.org | 0.0332 |
322ade57e77a7be3373fd049bc5b911539b3b2b43701f410cdfbf74d87ccc620 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models | Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federat... | [
"Linh Tran",
"Wei Sun",
"Stacy Patterson",
"Ana Milanova"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-01-23T00:00:00 | https://arxiv.org/abs/2501.13904 | https://arxiv.org/pdf/2501.13904v3 | 2501.13904 | 10.48550/arXiv.2501.13904 | 11 | 2 | false | null | International Conference on Learning Representations | 0.2698 |
6f067125ce00d4605e93f76e52bea90556059d3494b90c281f7665df07903a5c | [
"arxiv",
"semantic_scholar"
] | A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning | Federated learning (FL) has come forward as a critical approach for privacy-preserving machine learning in healthcare, allowing collaborative model training across decentralized medical datasets without exchanging clients' data. However, current security implementations for these systems face a fundamental trade-off: r... | [
"Abdulkadir Korkmaz",
"Praveen Rao"
] | [
"cs.CR",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2025-01-22T00:00:00 | https://arxiv.org/abs/2501.12911 | https://arxiv.org/pdf/2501.12911v4 | 2501.12911 | 10.1109/CCNC65079.2026.11366371 | 4 | 0 | false | null | Consumer Communications and Networking Conference | 0.1747 |
2a6bf616b413fe391610120075db6a11a886eff66a37656ad0567c9d3e29137a | [
"arxiv",
"semantic_scholar"
] | TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning | Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current research indicates that traditional federated learning platforms are unable to ensure privacy due to privacy leaks caused by t... | [
"Runhua Xu",
"Bo Li",
"Chao Li",
"James B. D. Joshi",
"Shuai Ma",
"Jianxin Li"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-01-09T00:00:00 | https://arxiv.org/abs/2501.05053 | https://arxiv.org/pdf/2501.05053v1 | 2501.05053 | 10.1109/TDSC.2024.3350206 | 36 | 2 | false | null | IEEE Transactions on Dependable and Secure Computing | 0.3921 |
fef0b3a2b9108b59024bf79ee189ecfdb9c6cb73f4b3356b5c1e05a268bd4cbf | [
"arxiv",
"semantic_scholar"
] | An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack | In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random For... | [
"Kunal Bhatnagar",
"Sagana Chattanathan",
"Angela Dang",
"Bhargav Eranki",
"Ronnit Rana",
"Charan Sridhar",
"Siddharth Vedam",
"Angie Yao",
"Mark Stamp"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-12-24T00:00:00 | https://arxiv.org/abs/2412.18507 | https://arxiv.org/pdf/2412.18507v1 | 2412.18507 | 10.48550/arXiv.2412.18507 | 2 | 0 | false | null | arXiv.org | 0.1193 |
bf89495e8b649a3b03efca8ef5d724668ba3a1551734a330373754cefb67ed31 | [
"arxiv",
"semantic_scholar"
] | Federal Learning Framework for Quality Evaluation of Blastomere Cleavage | This study addresses the issue of leveraging federated learning to improve data privacy and performance in IVF embryo selection. The EM (Expectation-Maximization) algorithm is incorporated into deep learning models to form a federated learning framework for quality evaluation of blastomere cleavage using two-dimensiona... | [
"Jung-Hua Wang",
"Huai-Wen Chang",
"Rong-Yu Wu",
"Ting-Yuan Wang",
"Ming-Jer Chen",
"Yu-Chiao Yi"
] | [
"eess.IV"
] | [
"Engineering"
] | 2024-12-21T00:00:00 | https://arxiv.org/abs/2412.16567 | https://arxiv.org/pdf/2412.16567v1 | 2412.16567 | null | 1 | 0 | false | null | null | 0.0753 |
a2cc18f06fa89e9f96a42d3bbd90bc8bb3281bbdec0a7ea754013075c317000d | [
"arxiv",
"semantic_scholar"
] | SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning | Federated Learning (FL) enables multiple clients to train a collaborative model without sharing their local data. Split Learning (SL) allows a model to be trained in a split manner across different locations. Split-Federated (SplitFed) learning is a more recent approach that combines the strengths of FL and SL. SplitFe... | [
"Chamani Shiranthika",
"Hadi Hadizadeh",
"Parvaneh Saeedi",
"Ivan V. Bajić"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-12-18T00:00:00 | https://arxiv.org/abs/2412.17150 | https://arxiv.org/pdf/2412.17150v1 | 2412.17150 | 10.48550/arXiv.2412.17150 | 5 | 0 | true | https://github.com/ChamaniS/SplitFedZip} | arXiv.org | 0.1945 |
13fbed6ad403d7d0f4f6c0afd4bc0752a85d7018d5bbc9e4eb234c825530b0dd | [
"arxiv",
"semantic_scholar"
] | Concurrent vertical and horizontal federated learning with fuzzy cognitive maps | Data privacy is a major concern in industries such as healthcare or finance. The requirement to safeguard privacy is essential to prevent data breaches and misuse, which can have severe consequences for individuals and organisations. Federated learning is a distributed machine learning approach where multiple participa... | [
"Jose L Salmeron",
"Irina Arévalo"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-12-17T00:00:00 | https://arxiv.org/abs/2412.12844 | https://arxiv.org/pdf/2412.12844v1 | 2412.12844 | 10.48550/arXiv.2412.12844 | 6 | 0 | false | null | arXiv.org | 0.2113 |
0ab4f3ff5dc1847cf836af6eed51edc29850d4cc310341f582bbe25982bbf362 | [
"arxiv",
"semantic_scholar"
] | Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning | Bidirectional privacy-preservation federated learning is crucial as both local gradients and the global model may leak privacy. However, only a few works attempt to achieve it, and they often face challenges such as excessive communication and computational overheads, or significant degradation of model accuracy, which... | [
"Xue Yang",
"Depan Peng",
"Yan Feng",
"Xiaohu Tang",
"Weijun Fang",
"Jun Shao"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2024-12-16T00:00:00 | https://arxiv.org/abs/2412.11737 | https://arxiv.org/pdf/2412.11737v1 | 2412.11737 | 10.48550/arXiv.2412.11737 | 2 | 0 | false | null | arXiv.org | 0.1193 |
88742b84bff7303e1a19cce2b41b38301c802a6036c97a6c0303be974e411f74 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks | Privacy-preserving federated learning (PPFL) aims to train a global model for multiple clients while maintaining their data privacy. However, current PPFL protocols exhibit one or more of the following insufficiencies: considerable degradation in accuracy, the requirement for sharing keys, and cooperation during the ke... | [
"Wenhan Dong",
"Chao Lin",
"Xinlei He",
"Shengmin Xu",
"Xinyi Huang"
] | [
"cs.CR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-12-02T00:00:00 | https://arxiv.org/abs/2412.01650 | https://arxiv.org/pdf/2412.01650v3 | 2412.01650 | 10.48550/arXiv.2412.01650 | 2 | 1 | false | null | Knowledge Science, Engineering and Management | 0.1505 |
670ad65fcacc71df89e9df2a7c71040bafde9ebd2322d14b7f1f30b8a0e5e5e8 | [
"arxiv",
"semantic_scholar"
] | Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture | With increasing concerns over privacy in healthcare, especially for sensitive medical data, this research introduces a federated learning framework that combines local differential privacy and secure aggregation using Secure Multi-Party Computation for medical image classification. Further, we propose DPResNet, a modif... | [
"Mohamad Haj Fares",
"Ahmed Mohamed Saad Emam Saad"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2024-12-01T00:00:00 | https://arxiv.org/abs/2412.00687 | https://arxiv.org/pdf/2412.00687v1 | 2412.00687 | 10.48550/arXiv.2412.00687 | 16 | 2 | false | null | arXiv.org | 0.3076 |
999f5b94a7f80dce590268679f4bcfb6dc784648b34b00390815bf51618a859d | [
"arxiv",
"semantic_scholar"
] | DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing | In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and personal information, which raises serious privacy concerns. It has bee... | [
"Utsab Saha",
"Tanvir Muntakim Tonoy",
"Hafiz Imtiaz"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2024-11-25T00:00:00 | https://arxiv.org/abs/2411.16121 | https://arxiv.org/pdf/2411.16121v3 | 2411.16121 | 10.1002/spy2.70207 | 1 | 0 | false | null | Security and Privacy | 0.0753 |
73ab88a5717167d4de840aa69f72836cec1072add1e4fe209c933339b39169d6 | [
"arxiv",
"semantic_scholar"
] | Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources | Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community w... | [
"Siddhant Dutta",
"Iago Leal de Freitas",
"Pedro Maciel Xavier",
"Claudio Miceli de Farias",
"David Esteban Bernal Neira"
] | [
"cs.LG",
"cs.DC",
"cs.NE"
] | [
"Computer Science"
] | 2024-11-23T00:00:00 | https://arxiv.org/abs/2411.16737 | https://arxiv.org/pdf/2411.16737v2 | 2411.16737 | 10.48550/arXiv.2411.16737 | 8 | 1 | false | null | null | 0.2386 |
39964fcb2388ff58a484d478345fde22187d45af7f51867aa4f66bb3be7c5412 | [
"arxiv",
"semantic_scholar"
] | FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning | Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing... | [
"Zhenyu Wen",
"Wanglei Feng",
"Di Wu",
"Haozhen Hu",
"Chang Xu",
"Bin Qian",
"Zhen Hong",
"Cong Wang",
"Shouling Ji"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.11713 | https://arxiv.org/pdf/2411.11713v1 | 2411.11713 | 10.1145/3690624.3709346 | 4 | 0 | false | null | Knowledge Discovery and Data Mining | 0.1747 |
bf1cf26e0aae43c02909fb9c8160d906199032a53beb5c4683248e5f5ee30a1f | [
"arxiv",
"semantic_scholar"
] | NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA | The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a federated setting for a real-life use case: invoice processing. The competition introduced a dataset of real invoice documents, along with assoc... | [
"Marlon Tobaben",
"Mohamed Ali Souibgui",
"Rubèn Tito",
"Khanh Nguyen",
"Raouf Kerkouche",
"Kangsoo Jung",
"Joonas Jälkö",
"Lei Kang",
"Andrey Barsky",
"Vincent Poulain d'Andecy",
"Aurélie Joseph",
"Aashiq Muhamed",
"Kevin Kuo",
"Virginia Smith",
"Yusuke Yamasaki",
"Takumi Fukami",
"... | [
"cs.LG",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2024-11-06T00:00:00 | https://arxiv.org/abs/2411.03730 | https://arxiv.org/pdf/2411.03730v2 | 2411.03730 | 10.48550/arXiv.2411.03730 | 4 | 0 | false | null | Transactions on Machine Learning Research, ISSN 2835-8856, 2025 | 0.1747 |
c3fd8c947b2178b44e1034c270a980fba30167da3362b3a9d0e06cf26fe33ac3 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering | Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. Howe... | [
"Fabrianne Effendi",
"Anupam Chattopadhyay"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-05T00:00:00 | https://arxiv.org/abs/2411.02926 | https://arxiv.org/pdf/2411.02926v2 | 2411.02926 | 10.48550/arXiv.2411.02926 | 11 | 2 | false | null | null | 0.2698 |
d9fd82a176508a1d3af086c11ba19e59df19c7ac2bd2ba73dae82b7552cb4c2d | [
"arxiv",
"semantic_scholar"
] | FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks | Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training m... | [
"Duong H. Nguyen",
"Phi L. Nguyen",
"Truong T. Nguyen",
"Hieu H. Pham",
"Duc A. Tran"
] | [
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2024-11-05T00:00:00 | https://arxiv.org/abs/2411.02773 | https://arxiv.org/pdf/2411.02773v1 | 2411.02773 | 10.1109/BigData62323.2024.10825703 | 5 | 0 | false | null | BigData Congress [Services Society] | 0.1945 |
6432e62057e6835bc48ae3acb09245ba356fa371aaf830bd10fc22f44c7a5d82 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing | Federated Learning (FL) has become a key method for preserving data privacy in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally while transmitting only model updates. Despite this design, FL remains susceptible to threats such as model inversion and membership inference attacks, ... | [
"Fardin Jalil Piran",
"Zhiling Chen",
"Mohsen Imani",
"Farhad Imani"
] | [
"cs.LG",
"cs.AI",
"cs.CR",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-11-02T00:00:00 | https://arxiv.org/abs/2411.01140 | https://arxiv.org/pdf/2411.01140v3 | 2411.01140 | 10.1016/j.compeleceng.2025.110261 | 18 | 0 | false | null | Computers & electrical engineering | 0.3197 |
667f4e1b444f70ee8d4bc2ac05db63eaa2753c34f55e62fc85dfa67710eea74e | [
"arxiv",
"semantic_scholar"
] | Optimizing Federated Learning by Entropy-Based Client Selection | Although deep learning has revolutionized domains such as natural language processing and computer vision, its dependence on centralized datasets raises serious privacy concerns. Federated learning addresses this issue by enabling multiple clients to collaboratively train a global deep learning model without compromisi... | [
"Andreas Lutz",
"Gabriele Steidl",
"Karsten Müller",
"Wojciech Samek"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-11-02T00:00:00 | https://arxiv.org/abs/2411.01240 | https://arxiv.org/pdf/2411.01240v3 | 2411.01240 | 10.1109/FLTA67013.2025.11336673 | 4 | 1 | false | null | null | 0.1747 |
eeb7487d1cc55d44d08bde6834cc2b79a44de90ef0c579c6615a304b78a1204d | [
"arxiv",
"semantic_scholar"
] | PARDON: Privacy-Aware and Robust Federated Domain Generalization | Federated Learning (FL) shows promise in preserving privacy and enabling collaborative learning. However, most current solutions focus on private data collected from a single domain. A significant challenge arises when client data comes from diverse domains (i.e., domain shift), leading to poor performance on unseen do... | [
"Dung Thuy Nguyen",
"Taylor T. Johnson",
"Kevin Leach"
] | [
"cs.LG",
"cs.CV",
"cs.DC"
] | [
"Computer Science"
] | 2024-10-30T00:00:00 | https://arxiv.org/abs/2410.22622 | https://arxiv.org/pdf/2410.22622v2 | 2410.22622 | 10.1109/ICDCS63083.2025.00074 | 0 | 0 | true | https://github.com/judydnguyen/PARDON-FedDG | IEEE International Conference on Distributed Computing Systems | 0 |
e987d1ee39164a630c3287ebc422706e373227169954cec875a60831dd8a0891 | [
"arxiv",
"semantic_scholar"
] | Comparative Evaluation of Clustered Federated Learning Methods | Over recent years, Federated Learning (FL) has proven to be one of the most promising methods of distributed learning which preserves data privacy. As the method evolved and was confronted to various real-world scenarios, new challenges have emerged. One such challenge is the presence of highly heterogeneous (often ref... | [
"Michael Ben Ali",
"Omar El-Rifai",
"Imen Megdiche",
"André Peninou",
"Olivier Teste"
] | [
"stat.ML",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-18T00:00:00 | https://arxiv.org/abs/2410.14212 | https://arxiv.org/pdf/2410.14212v2 | 2410.14212 | 10.48550/arXiv.2410.14212 | 0 | 0 | false | null | The 2nd IEEE International Conference on Federated Learning Technologies and Applications (FLTA24), Sep 2024, Valencia (Espagne), Spain | 0 |
9aff22b5f9195586527be1778f1a4d72e829583e3ce86fcbc369ab7f0707c8a6 | [
"arxiv",
"semantic_scholar"
] | Disentangling data distribution for Federated Learning | Federated Learning (FL) facilitates collaborative training of a global model whose performance is boosted by private data owned by distributed clients, without compromising data privacy. Yet the wide applicability of FL is hindered by entanglement of data distributions across different clients. This paper demonstrates ... | [
"Xinyuan Zhao",
"Hanlin Gu",
"Lixin Fan",
"Yuxing Han",
"Qiang Yang"
] | [
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-10-16T00:00:00 | https://arxiv.org/abs/2410.12530 | https://arxiv.org/pdf/2410.12530v2 | 2410.12530 | 10.48550/arXiv.2410.12530 | 1 | 0 | false | null | arXiv.org | 0.0753 |
b2af53abc04972636adaaea9c4d32608d035a0b4f394c28cbbb0a8f3d0c41086 | [
"arxiv",
"semantic_scholar"
] | Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure | This paper addresses the critical challenge of unlearning in Vertical Federated Learning (VFL), a setting that has received far less attention than its horizontal counterpart. Specifically, we propose the first method tailored to \textit{label unlearning} in VFL, where labels play a dual role as both essential inputs a... | [
"Hanlin Gu",
"Hong Xi Tae",
"Lixin Fan",
"Chee Seng Chan"
] | [
"cs.LG",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10922 | https://arxiv.org/pdf/2410.10922v4 | 2410.10922 | null | 5 | 1 | true | https://github.com/bryanhx/Towards-Privacy-Guaranteed-Label-Unlearning-in-Vertical-Federated-Learning | null | 0.1945 |
88e62efdfdecf6da42b59be140d004bcb67eaacbd6dfb329cd242538f1c7572f | [
"arxiv",
"semantic_scholar"
] | Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees | Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, colla... | [
"Xin Yu",
"Zelin He",
"Ying Sun",
"Lingzhou Xue",
"Runze Li"
] | [
"stat.ML",
"cs.DC",
"cs.LG",
"math.ST",
"stat.CO"
] | [
"Computer Science",
"Mathematics"
] | 2024-10-11T00:00:00 | https://arxiv.org/abs/2410.08934 | https://arxiv.org/pdf/2410.08934v4 | 2410.08934 | null | 2 | 0 | false | null | International Conference on Machine Learning | 0.1193 |
d939da4e0ecff205e75b138fab216aa4c638802a285b454efd293de4bb104334 | [
"arxiv",
"semantic_scholar"
] | Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System | The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated L... | [
"Ravi Madduri",
"Zilinghan Li",
"Tarak Nandi",
"Kibaek Kim",
"Minseok Ryu",
"Alex Rodriguez"
] | [
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2024-09-29T00:00:00 | https://arxiv.org/abs/2409.19756 | https://arxiv.org/pdf/2409.19756v1 | 2409.19756 | 10.1109/TPS-ISA62245.2024.00039 | 5 | 1 | false | null | International Conference on Trust, Privacy and Security in Intelligent Systems and Applications | 0.1945 |
dde035c2e181795201f684f95878d44aff087558fac20d3740aa74c474dfa7ef | [
"arxiv",
"semantic_scholar"
] | In-depth Analysis of Privacy Threats in Federated Learning for Medical Data | Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, recent studies have revealed that the default settings of federated learni... | [
"Badhan Chandra Das",
"M. Hadi Amini",
"Yanzhao Wu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-09-27T00:00:00 | https://arxiv.org/abs/2409.18907 | https://arxiv.org/pdf/2409.18907v1 | 2409.18907 | 10.48550/arXiv.2409.18907 | 2 | 0 | false | null | arXiv.org | 0.1193 |
876b4f9b8fa3491ba8095c9fe09a178c405a327272561abad41de88ea2575c13 | [
"arxiv",
"semantic_scholar"
] | Immersion and Invariance-based Coding for Privacy-Preserving Federated Learning | Federated learning (FL) has emerged as a method to preserve privacy in collaborative distributed learning. In FL, clients train AI models directly on their devices rather than sharing data with a centralized server, which can pose privacy risks. However, it has been shown that despite FL's partial protection of local d... | [
"Haleh Hayati",
"Carlos Murguia",
"Nathan van de Wouw"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2024-09-25T00:00:00 | https://arxiv.org/abs/2409.17201 | https://arxiv.org/pdf/2409.17201v2 | 2409.17201 | 10.48550/arXiv.2409.17201 | 0 | 0 | false | null | arXiv.org | 0 |
73c2c40fca3f31cb841930020b024688e41ccb3af6b725db4268cd6146d87497 | [
"arxiv",
"semantic_scholar"
] | Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework | We present Flotta, a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts requiring high levels of security, such as the biomedical field. Flotta is a Python package, inspired in several aspects by Apache Spa... | [
"Claudio Bonesana",
"Daniele Malpetti",
"Sandra Mitrović",
"Francesca Mangili",
"Laura Azzimonti"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-09-20T00:00:00 | https://arxiv.org/abs/2409.13473 | https://arxiv.org/pdf/2409.13473v1 | 2409.13473 | 10.1109/FLTA63145.2024.10840050 | 1 | 0 | false | null | null | 0.0753 |
dd457fae2452898cbc35f574932b55d9826b04dbe4560fe32b972976bd5b508f | [
"arxiv",
"semantic_scholar"
] | Global Outlier Detection in a Federated Learning Setting with Isolation Forest | We present a novel strategy for detecting global outliers in a federated learning setting, targeting in particular cross-silo scenarios. Our approach involves the use of two servers and the transmission of masked local data from clients to one of the servers. The masking of the data prevents the disclosure of sensitive... | [
"Daniele Malpetti",
"Laura Azzimonti"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-09-20T00:00:00 | https://arxiv.org/abs/2409.13466 | https://arxiv.org/pdf/2409.13466v1 | 2409.13466 | 10.1109/FLTA63145.2024.10840168 | 2 | 0 | false | null | null | 0.1193 |
2c0470d4dee1ec65207ebb530e34196f93000b61720f89cc96271fb37fdb4bfa | [
"arxiv",
"semantic_scholar"
] | Data Poisoning and Leakage Analysis in Federated Learning | Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating threats: {\em training data privacy intrusion} and {\em training data poisoning}. We first investigate training data privacy threat and pre... | [
"Wenqi Wei",
"Tiansheng Huang",
"Zachary Yahn",
"Anoop Singhal",
"Margaret Loper",
"Ling Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.13004 | https://arxiv.org/pdf/2409.13004v1 | 2409.13004 | 10.1007/978-3-031-58923-2_3 | 3 | 0 | false | null | arXiv.org | 0.1505 |
b51ec9a189ac41166b1bf376c920917eb06ba05bd9aea97d1f0e9f8dd4f022f8 | [
"arxiv",
"semantic_scholar"
] | Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data | This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy ... | [
"Manuel Röder",
"Frank-Michael Schleif"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.12575 | https://arxiv.org/pdf/2409.12575v1 | 2409.12575 | 10.48550/arXiv.2409.12575 | 0 | 0 | false | null | null | 0 |
a036553d41a49fa57c42f651062d563d06229038bad92f966f26f8af60445efc | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator | Federated Learning (FL) is gaining popularity as a distributed learning framework that only shares model parameters or gradient updates and keeps private data locally. However, FL is at risk of privacy leakage caused by privacy inference attacks. And most existing privacy-preserving mechanisms in FL conflict with achie... | [
"Kangyang Luo",
"Shuai Wang",
"Xiang Li",
"Yunshi Lan",
"Ming Gao",
"Jinlong Shu"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2024-09-11T00:00:00 | https://arxiv.org/abs/2409.06955 | https://arxiv.org/pdf/2409.06955v2 | 2409.06955 | 10.48550/arXiv.2409.06955 | 2 | 0 | false | null | arXiv.org | 0.1193 |
da3176728c186e3d4604f510c332aac0f53dfb431b383d93ea9a0b9e76192fd0 | [
"arxiv",
"semantic_scholar"
] | Buffer-based Gradient Projection for Continual Federated Learning | Continual Federated Learning (CFL) is essential for enabling real-world applications where multiple decentralized clients adaptively learn from continuous data streams. A significant challenge in CFL is mitigating catastrophic forgetting, where models lose previously acquired knowledge when learning new information. Ex... | [
"Shenghong Dai",
"Jy-yong Sohn",
"Yicong Chen",
"S M Iftekharul Alam",
"Ravikumar Balakrishnan",
"Suman Banerjee",
"Nageen Himayat",
"Kangwook Lee"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2024-09-03T00:00:00 | https://arxiv.org/abs/2409.01585 | https://arxiv.org/pdf/2409.01585v1 | 2409.01585 | 10.48550/arXiv.2409.01585 | 4 | 0 | true | https://github.com/shenghongdai/Fed-A-GEM | null | 0.1747 |
eac7d37eedfc108123b61091580aa598474de47d4ccea90f17a9869ee83e9dcd | [
"arxiv",
"semantic_scholar"
] | Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications | Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of secure aggregation (SA) schemes to provide privacy guarantees over the... | [
"Riccardo Taiello",
"Sergen Cansiz",
"Marc Vesin",
"Francesco Cremonesi",
"Lucia Innocenti",
"Melek Önen",
"Marco Lorenzi"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2024-09-02T00:00:00 | https://arxiv.org/abs/2409.00974 | https://arxiv.org/pdf/2409.00974v1 | 2409.00974 | 10.48550/arXiv.2409.00974 | 8 | 2 | true | null | null | 0.2386 |
23057af213efe2eab54b612098b9f0b77faa1d104b97581d9c4a2ac478c49e9c | [
"arxiv",
"semantic_scholar"
] | Seamless Integration: Sampling Strategies in Federated Learning Systems | Federated Learning (FL) represents a paradigm shift in the field of machine learning, offering an approach for a decentralized training of models across a multitude of devices while maintaining the privacy of local data. However, the dynamic nature of FL systems, characterized by the ongoing incorporation of new client... | [
"Tatjana Legler",
"Vinit Hegiste",
"Martin Ruskowski"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-08-18T00:00:00 | https://arxiv.org/abs/2408.09545 | https://arxiv.org/pdf/2408.09545v2 | 2408.09545 | 10.1109/FLTA63145.2024.10840172 | 3 | 0 | false | null | null | 0.1505 |
acf252f00df7e55ad82f8f694044d907cc3256343f2974aff56f68d65ea54981 | [
"arxiv",
"semantic_scholar"
] | A Multivocal Literature Review on Privacy and Fairness in Federated Learning | Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted during training, making additional privacy-preserving measures such as differential privacy imperative. To implement real-world federated lear... | [
"Beatrice Balbierer",
"Lukas Heinlein",
"Domenique Zipperling",
"Niklas Kühl"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-08-16T00:00:00 | https://arxiv.org/abs/2408.08666 | https://arxiv.org/pdf/2408.08666v2 | 2408.08666 | 10.48550/arXiv.2408.08666 | 2 | 0 | false | null | Wirtschaftsinformatik | 0.1193 |
69482f4190ccaf7293df61c501a350a32b5c4dad132c0e5bf237892316dce98b | [
"arxiv",
"semantic_scholar"
] | An Adaptive Differential Privacy Method Based on Federated Learning | Differential privacy is one of the methods to solve the problem of privacy protection in federated learning. Setting the same privacy budget for each round will result in reduced accuracy in training. The existing methods of the adjustment of privacy budget consider fewer influencing factors and tend to ignore the boun... | [
"Zhiqiang Wang",
"Xinyue Yu",
"Qianli Huang",
"Yongguang Gong"
] | [
"cs.CR",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-08-13T00:00:00 | https://arxiv.org/abs/2408.08909 | https://arxiv.org/pdf/2408.08909v1 | 2408.08909 | 10.48550/arXiv.2408.08909 | 4 | 0 | false | null | arXiv.org | 0.1747 |
fc292dab9ef66651088d4c8162392b37371d41658a4487148eb798ea1b091133 | [
"arxiv",
"semantic_scholar"
] | Privacy in Federated Learning | Federated Learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices. However, FL introduces new privacy challenges, as model up... | [
"Jaydip Sen",
"Hetvi Waghela",
"Sneha Rakshit"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-08-12T00:00:00 | https://arxiv.org/abs/2408.08904 | https://arxiv.org/pdf/2408.08904v1 | 2408.08904 | 10.5772/intechopen.1003421 | 15 | 1 | false | null | arXiv.org | 0.301 |
02555f018c7bfa30343479107f9990084a2d609d28f7da30fde07842c9f17751 | [
"arxiv",
"semantic_scholar"
] | Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption | In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across mul... | [
"Siyang Jiang",
"Hao Yang",
"Qipeng Xie",
"Chuan Ma",
"Sen Wang",
"Guoliang Xing"
] | [
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2024-08-12T00:00:00 | https://arxiv.org/abs/2408.06197 | https://arxiv.org/pdf/2408.06197v1 | 2408.06197 | 10.48550/arXiv.2408.06197 | 12 | 1 | false | null | arXiv.org | 0.2785 |
e77bff5686541a56899aa65b98984e4f4e941cdd25fb5899896e2f65536a9ab1 | [
"arxiv",
"semantic_scholar"
] | Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions | Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which are exacerbated in critical domains (e.g., healthcare). Moreover, certa... | [
"Michele Miranda",
"Elena Sofia Ruzzetti",
"Andrea Santilli",
"Fabio Massimo Zanzotto",
"Sébastien Bratières",
"Emanuele Rodolà"
] | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-10T00:00:00 | https://arxiv.org/abs/2408.05212 | https://arxiv.org/pdf/2408.05212v2 | 2408.05212 | 10.48550/arXiv.2408.05212 | 24 | 0 | false | null | arXiv.org | 0.3495 |
023f623097a4b612af5343f11b854aa2a4711726a4cad9ed747715aa6dbd55d4 | [
"arxiv",
"semantic_scholar"
] | Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion | The rapid growth of graph-structured data necessitates partitioning and distributed storage across decentralized systems, driving the emergence of federated graph learning to collaboratively train Graph Neural Networks (GNNs) without compromising privacy. However, current methods exhibit limited performance when handli... | [
"Linfeng Luo",
"Zhiqi Guo",
"Fengxiao Tang",
"Zihao Qiu",
"Ming Zhao"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2024-08-09T00:00:00 | https://arxiv.org/abs/2408.05160 | https://arxiv.org/pdf/2408.05160v3 | 2408.05160 | 10.1109/ICDM65498.2025.00149 | 0 | 0 | false | null | Industrial Conference on Data Mining | 0 |
d71a6d104c635b41c6746018bf991dc8f733ec48a25ef65452f5a7352cbe881f | [
"arxiv",
"semantic_scholar"
] | Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection | We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to g... | [
"Md. Saikat Islam Khan",
"Aparna Gupta",
"Oshani Seneviratne",
"Stacy Patterson"
] | [
"cs.CE"
] | [
"Computer Science"
] | 2024-08-03T00:00:00 | https://arxiv.org/abs/2408.01609 | https://arxiv.org/pdf/2408.01609v1 | 2408.01609 | 10.1109/CIFEr62890.2024.10772978 | 11 | 1 | false | null | IEEE Conference on Computational Intelligence for Financial Engineering & Economics | 0.2698 |
a5a86791e29191764cf8194488ae1e540f9a6c412946b7af50e58afd25afdebd | [
"arxiv",
"semantic_scholar"
] | Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory | Federated learning has emerged as a promising paradigm for collaborative model training while preserving data privacy. However, recent studies have shown that it is vulnerable to various privacy attacks, such as data reconstruction attacks. In this paper, we provide a theoretical analysis of privacy leakage in federate... | [
"Xiaojin Zhang",
"Wei Chen"
] | [
"cs.CR",
"cs.AI",
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-07-23T00:00:00 | https://arxiv.org/abs/2407.16735 | https://arxiv.org/pdf/2407.16735v1 | 2407.16735 | 10.48550/arXiv.2407.16735 | 2 | 0 | false | null | arXiv.org | 0.1193 |
745a3245e16f24d5b3fa08c6410438e2a2d2b0409e0f0156b6c3b4e3f4e0304a | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving gradient-based fair federated learning | Federated learning (FL) schemes allow multiple participants to collaboratively train neural networks without the need to directly share the underlying data.However, in early schemes, all participants eventually obtain the same model. Moreover, the aggregation is typically carried out by a third party, who obtains combi... | [
"Janis Adamek",
"Moritz Schulze Darup"
] | [
"cs.LG",
"cs.CR",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2024-07-18T00:00:00 | https://arxiv.org/abs/2407.13881 | https://arxiv.org/pdf/2407.13881v1 | 2407.13881 | 10.1109/CoDIT62066.2024.10708141 | 2 | 0 | false | null | International Conference on Control, Decision and Information Technologies | 0.1193 |
76f475c3166914f56c84addf5def2fcf1c0a03b4aec698eee711bf8cb505e3e5 | [
"arxiv",
"semantic_scholar"
] | Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing | Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus t... | [
"Cui Zhang",
"Wenjun Zhang",
"Qiong Wu",
"Pingyi Fan",
"Qiang Fan",
"Jiangzhou Wang",
"Khaled B. Letaief"
] | [
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08462 | https://arxiv.org/pdf/2407.08462v2 | 2407.08462 | 10.1109/JIOT.2024.3447036 | 93 | 1 | true | https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient | IEEE Internet of Things Journal | 0.4933 |
a785f72c7f8b503a90f8390bc0723ec13d831a192c201bb9a6875a806376e83d | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version) | Deduplication is a vital preprocessing step that enhances machine learning model performance and saves training time and energy. However, enhancing federated learning through deduplication poses challenges, especially regarding scalability and potential privacy violations if deduplication involves sharing all clients' ... | [
"Aydin Abadi",
"Vishnu Asutosh Dasu",
"Sumanta Sarkar"
] | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08152 | https://arxiv.org/pdf/2407.08152v2 | 2407.08152 | 10.48550/arXiv.2407.08152 | 6 | 0 | false | null | IACR Cryptology ePrint Archive | 0.2113 |
f8fc65ce496a774267d11a6f30f1a5bd5b184bc27aad819537baf93f946840ce | [
"arxiv",
"semantic_scholar"
] | Collection, usage and privacy of mobility data in the enterprise and public administrations | Human mobility data is a crucial resource for urban mobility management, but it does not come without personal reference. The implementation of security measures such as anonymization is thus needed to protect individuals' privacy. Often, a trade-off arises as such techniques potentially decrease the utility of the dat... | [
"Alexandra Kapp"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-07-04T00:00:00 | https://arxiv.org/abs/2407.03732 | https://arxiv.org/pdf/2407.03732v1 | 2407.03732 | 10.56553/popets-2022-0117 | 8 | 0 | false | null | Proceedings on Privacy Enhancing Technologies | 0.2386 |
13ae3da2dc9c8e95755d04434c955db908dfa4a047b8ddabf9e4a164be5c4d1a | [
"arxiv",
"semantic_scholar"
] | Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning | With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Ed... | [
"Wenhua Wang",
"Qiong Wu",
"Pingyi Fan",
"Nan Cheng",
"Wen Chen",
"Jiangzhou Wang",
"Khaled B. Letaief"
] | [
"cs.LG",
"cs.DC",
"cs.MA",
"cs.NI"
] | [
"Computer Science"
] | 2024-07-01T00:00:00 | https://arxiv.org/abs/2407.02342 | https://arxiv.org/pdf/2407.02342v1 | 2407.02342 | 10.48550/arXiv.2407.02342 | 8 | 0 | true | https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning | arXiv.org | 0.2386 |
6c6102b40489852f77fbb7ad6474d892584aa7d75fea2e0ad1c96c48c7faa616 | [
"arxiv",
"semantic_scholar"
] | Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy | An Electronic Health Record (EHR) is an electronic database used by healthcare providers to store patients' medical records which may include diagnoses, treatments, costs, and other personal information. Machine learning (ML) algorithms can be used to extract and analyze patient data to improve patient care. Patient re... | [
"Naif A. Ganadily",
"Han J. Xia"
] | [
"cs.LG",
"cs.CR",
"cs.ET"
] | [
"Computer Science"
] | 2024-06-23T00:00:00 | https://arxiv.org/abs/2406.15962 | https://arxiv.org/pdf/2406.15962v1 | 2406.15962 | 10.48550/arXiv.2406.15962 | 5 | 0 | false | null | arXiv.org | 0.1945 |
a07bc6cf64196611595ff6875776f6c1b8944d9eaefad22d7d70ded9140ae76e | [
"arxiv",
"semantic_scholar"
] | Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation | Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improving disease diagnoses, treatment planning, and post-treatment monitoring. Various computer vision tasks like image classification, object de... | [
"Nikolas Koutsoubis",
"Yasin Yilmaz",
"Ravi P. Ramachandran",
"Matthew Schabath",
"Ghulam Rasool"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"eess.IV",
"stat.ML"
] | [
"Computer Science",
"Engineering",
"Mathematics"
] | 2024-06-18T00:00:00 | https://arxiv.org/abs/2406.12815 | https://arxiv.org/pdf/2406.12815v1 | 2406.12815 | 10.48550/arXiv.2406.12815 | 26 | 1 | false | null | arXiv.org | 0.3578 |
4d4adaf0fea459330849e892166531c86068e8ac44fb8dcf59fbd077e99b2577 | [
"arxiv",
"semantic_scholar"
] | Promoting Data and Model Privacy in Federated Learning through Quantized LoRA | Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for parameter updates during the learning process. However, the development of large language models (LLMs) requires substantial data and computational ... | [
"JianHao Zhu",
"Changze Lv",
"Xiaohua Wang",
"Muling Wu",
"Wenhao Liu",
"Tianlong Li",
"Zixuan Ling",
"Cenyuan Zhang",
"Xiaoqing Zheng",
"Xuanjing Huang"
] | [
"cs.LG",
"cs.CL",
"cs.CR"
] | [
"Computer Science"
] | 2024-06-16T00:00:00 | https://arxiv.org/abs/2406.10976 | https://arxiv.org/pdf/2406.10976v1 | 2406.10976 | 10.48550/arXiv.2406.10976 | 14 | 1 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.294 |
1282ee9230196185d57ad379f5af61cea0fe36d3fa8e3b2518f0cabbf611f7fa | [
"arxiv",
"semantic_scholar"
] | A deep cut into Split Federated Self-supervised Learning | Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. However, state-of-the-art methods, such as MocoSFL, are optimized for network division at the initial layers, which decreases the protection ... | [
"Marcin Przewięźlikowski",
"Marcin Osial",
"Bartosz Zieliński",
"Marek Śmieja"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-06-12T00:00:00 | https://arxiv.org/abs/2406.08267 | https://arxiv.org/pdf/2406.08267v2 | 2406.08267 | 10.1007/978-3-031-70344-7_26 | 1 | 0 | false | null | Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science, vol 14942. Springer, Cham | 0.0753 |
3a04a9d592df7ac97428db9351b5cde08779bfb3992ecf832e79c407aa534220 | [
"arxiv",
"semantic_scholar"
] | Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints | This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both varying sample sizes and differential privacy constraints across serv... | [
"T. Tony Cai",
"Abhinav Chakraborty",
"Lasse Vuursteen"
] | [
"math.ST",
"cs.LG",
"stat.ML"
] | [
"Mathematics",
"Computer Science"
] | 2024-06-10T00:00:00 | https://arxiv.org/abs/2406.06755 | https://arxiv.org/pdf/2406.06755v1 | 2406.06755 | 10.48550/arXiv.2406.06755 | 13 | 1 | false | null | arXiv.org | 0.2865 |
46c45f3dc3d235098bd289f55f6883b13042f2620937f12b483073961fcbc28f | [
"arxiv",
"semantic_scholar"
] | Parameterizing Federated Continual Learning for Reproducible Research | Federated Learning (FL) systems evolve in heterogeneous and ever-evolving environments that challenge their performance. Under real deployments, the learning tasks of clients can also evolve with time, which calls for the integration of methodologies such as Continual Learning. To enable research reproducibility, we pr... | [
"Bart Cox",
"Jeroen Galjaard",
"Aditya Shankar",
"Jérémie Decouchant",
"Lydia Y. Chen"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2024-06-04T00:00:00 | https://arxiv.org/abs/2406.02015 | https://arxiv.org/pdf/2406.02015v1 | 2406.02015 | 10.48550/arXiv.2406.02015 | 1 | 0 | false | null | null | 0.0753 |
442af14978e28e9f7f2126250fc87c254d819aa6a84d703f1936ee4476ebbd5a | [
"arxiv",
"semantic_scholar"
] | No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning | Federated learning allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional federated learning is vulnerable to poisoning attacks, which can not only decrease the model performance, but also implant malicious backdoors. In additi... | [
"Zhibo Xing",
"Zijian Zhang",
"Zi'ang Zhang",
"Jiamou Liu",
"Liehuang Zhu",
"Giovanni Russello"
] | [
"cs.CR",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01080 | https://arxiv.org/pdf/2406.01080v1 | 2406.01080 | 10.48550/arXiv.2406.01080 | 6 | 1 | false | null | arXiv.org | 0.2113 |
49db7920d72abb78c3198b975dac4152a51eefd1ec2826f1a8be97b6c9844939 | [
"arxiv",
"semantic_scholar"
] | FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation | Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions, has triggered numerous follow-up research in designing powerful att... | [
"Hanlin Gu",
"Jiahuan Luo",
"Yan Kang",
"Yuan Yao",
"Gongxi Zhu",
"Bowen Li",
"Lixin Fan",
"Qiang Yang"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2024-06-03T00:00:00 | https://arxiv.org/abs/2406.01085 | https://arxiv.org/pdf/2406.01085v1 | 2406.01085 | 10.48550/arXiv.2406.01085 | 3 | 0 | false | null | arXiv.org | 0.1505 |
6592831b08c299c49e709b9d5bd7b18d102074926c94ada9af20726b47fb0ae8 | [
"arxiv",
"semantic_scholar"
] | Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy | In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning. Specifically, in the proposed feature-... | [
"Feng Wang",
"M. Cenk Gursoy",
"Senem Velipasalar"
] | [
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2024-05-15T00:00:00 | https://arxiv.org/abs/2405.09014 | https://arxiv.org/pdf/2405.09014v1 | 2405.09014 | 10.1109/TMLCN.2024.3408131 | 5 | 0 | false | null | IEEE Transactions on Machine Learning in Communications and Networking | 0.1945 |
21000fec970cd7b001f7b29cac5020d6f3d665bbec733d0e4709418022d1a046 | [
"arxiv",
"semantic_scholar"
] | SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning | In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggreg... | [
"Riyasat Ohib",
"Bishal Thapaliya",
"Gintare Karolina Dziugaite",
"Jingyu Liu",
"Vince Calhoun",
"Sergey Plis"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-05-15T00:00:00 | https://arxiv.org/abs/2405.09037 | https://arxiv.org/pdf/2405.09037v2 | 2405.09037 | null | 1 | 0 | false | null | Transactions on Machine Learning Research, 2026 | 0.0753 |
ca6ec05114bca7ed2fdc484c548b2d8b2b20108571f35fed2dd64e86e0eef6c2 | [
"arxiv",
"semantic_scholar"
] | Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data | The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving the privacy of clients' data. However, FL struggles with a significant performance reduction and poor convergence when confronted with Non-In... | [
"Mahdi Morafah",
"Matthias Reisser",
"Bill Lin",
"Christos Louizos"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-05-13T00:00:00 | https://arxiv.org/abs/2405.07925 | https://arxiv.org/pdf/2405.07925v1 | 2405.07925 | 10.48550/arXiv.2405.07925 | 13 | 1 | false | null | arXiv.org | 0.2865 |
74ea6cf2f9b91858de48290e5a68516fdc8fbdf0c154684bac5599a807da0397 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems | Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients' mobile dev... | [
"Amin Aminifar",
"Matin Shokri",
"Amir Aminifar"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2024-05-09T00:00:00 | https://arxiv.org/abs/2405.05611 | https://arxiv.org/pdf/2405.05611v2 | 2405.05611 | 10.1016/j.future.2024.07.035 | 71 | 2 | false | null | Future generations computer systems | 0.4643 |
86b40b9573bfdb002098ff52d2d9877a07a90f04f30706c09a407c54e3b6f698 | [
"arxiv",
"semantic_scholar"
] | Quantum Federated Learning Experiments in the Cloud with Data Encoding | Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations.... | [
"Shiva Raj Pokhrel",
"Naman Yash",
"Jonathan Kua",
"Gang Li",
"Lei Pan"
] | [
"cs.LG",
"cs.ET",
"quant-ph"
] | [
"Computer Science",
"Physics"
] | 2024-05-01T00:00:00 | https://arxiv.org/abs/2405.00909 | https://arxiv.org/pdf/2405.00909v1 | 2405.00909 | 10.48550/arXiv.2405.00909 | 13 | 0 | true | null | arXiv.org | 0.2865 |
9351a8d76b70331868b4ff7f3bbd2c03aa3d9838f25d242c1f866e27e495b7d5 | [
"arxiv",
"semantic_scholar"
] | Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning | Decentralized Multi-agent Learning (DML) enables collaborative model training while preserving data privacy. However, inherent heterogeneity in agents' resources (computation, communication, and task size) may lead to substantial variations in training time. This heterogeneity creates a bottleneck, lengthening the over... | [
"Seyed Mahmoud Sajjadi Mohammadabadi",
"Lei Yang",
"Feng Yan",
"Junshan Zhang"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.MA",
"cs.PF"
] | [
"Computer Science"
] | 2024-05-01T00:00:00 | https://arxiv.org/abs/2405.00839 | https://arxiv.org/pdf/2405.00839v1 | 2405.00839 | 10.1109/ICDCS60910.2024.00069 | 19 | 1 | false | null | IEEE International Conference on Distributed Computing Systems | 0.3253 |
f108924797a8007acb38d5f97b2e106c95ef1bc5b941f4aeea91c6eb4622c605 | [
"arxiv",
"semantic_scholar"
] | Harnessing Federated Generative Learning for Green and Sustainable Internet of Things | The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introd... | [
"Yuanhang Qi",
"M. Shamim Hossain"
] | [
"cs.NI",
"cs.AI"
] | [
"Computer Science"
] | 2024-04-30T00:00:00 | https://arxiv.org/abs/2407.05915 | https://arxiv.org/pdf/2407.05915v1 | 2407.05915 | 10.1016/j.jnca.2023.103812 | 16 | 1 | false | null | Journal of Network and Computer Applications | 0.3076 |
b0df9fbba38d3cdc1acec350c50c90f83a189760f5837704173a1db8e79742b1 | [
"arxiv",
"semantic_scholar"
] | Secure and Privacy-Preserving Authentication for Data Subject Rights Enforcement | In light of the GDPR, data controllers (DC) need to allow data subjects (DS) to exercise certain data subject rights. A key requirement here is that DCs can reliably authenticate a DS. Due to a lack of clear technical specifications, this has been realized in different ways, such as by requesting copies of ID documents... | [
"Malte Hansen",
"Andre Büttner"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2024-04-24T00:00:00 | https://arxiv.org/abs/2404.15859 | https://arxiv.org/pdf/2404.15859v1 | 2404.15859 | 10.1007/978-3-031-57978-3_12 | 0 | 0 | false | null | Privacy and Identity Management. Sharing in a Digital World. Privacy and Identity 2023. IFIP Advances in Information and Communication Technology, vol 695. Springer, Cham | 0 |
277a1bda802560883ce36ad7e2bf1f0e9ec44745c586a5b6a73bbf76e2c4333c | [
"arxiv",
"semantic_scholar"
] | A Federated Learning Approach to Privacy Preserving Offensive Language Identification | The spread of various forms of offensive speech online is an important concern in social media. While platforms have been investing heavily in ways of coping with this problem, the question of privacy remains largely unaddressed. Models trained to detect offensive language on social media are trained and/or fine-tuned ... | [
"Marcos Zampieri",
"Damith Premasiri",
"Tharindu Ranasinghe"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-17T00:00:00 | https://arxiv.org/abs/2404.11470 | https://arxiv.org/pdf/2404.11470v1 | 2404.11470 | 10.48550/arXiv.2404.11470 | 6 | 0 | false | null | Workshop on Trolling, Aggression and Cyberbullying | 0.2113 |
d5bbf20922a7c05fa10172ea408442b7ee5e581cf37afe1db720d01a38333810 | [
"arxiv",
"semantic_scholar"
] | Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection | Distributed training can facilitate the processing of large medical image datasets, and improve the accuracy and efficiency of disease diagnosis while protecting patient privacy, which is crucial for achieving efficient medical image analysis and accelerating medical research progress. This paper presents an innovative... | [
"Lisang Zhou",
"Meng Wang",
"Ning Zhou"
] | [
"eess.IV",
"cs.CR",
"cs.LG"
] | [
"Engineering",
"Computer Science"
] | 2024-04-15T00:00:00 | https://arxiv.org/abs/2404.10026 | https://arxiv.org/pdf/2404.10026v1 | 2404.10026 | 10.48550/arXiv.2404.10026 | 70 | 6 | false | null | Journal of Information, Technology and Policy (2023): 1-12 | 0.4628 |
50144da288ff12f54a0aa8f973f6fc23bda0c9dd74cf4833f8176194d726f59b | [
"arxiv",
"semantic_scholar"
] | On the Efficiency of Privacy Attacks in Federated Learning | Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy attack success rate and overlook the high computation costs for recovering private data, making the privacy attack impractical in real applicat... | [
"Nawrin Tabassum",
"Ka-Ho Chow",
"Xuyu Wang",
"Wenbin Zhang",
"Yanzhao Wu"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-15T00:00:00 | https://arxiv.org/abs/2404.09430 | https://arxiv.org/pdf/2404.09430v1 | 2404.09430 | 10.1109/CVPRW63382.2024.00426 | 5 | 0 | true | https://github.com/mlsysx/EPAFL | null | 0.1945 |
9cae3d88647b188ee3f98f800c7996d8108f28f771a06d95fabd0871f2e4afee | [
"arxiv",
"semantic_scholar"
] | Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning | While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Merging distributed computing with cryptographic techniques, decentralized technologies introduce a novel computin... | [
"Ji Liu",
"Chunlu Chen",
"Yu Li",
"Lin Sun",
"Yulun Song",
"Jingbo Zhou",
"Bo Jing",
"Dejing Dou"
] | [
"cs.CR",
"cs.AI",
"cs.DC",
"cs.LG"
] | [
"Computer Science"
] | 2024-03-28T00:00:00 | https://arxiv.org/abs/2403.19178 | https://arxiv.org/pdf/2403.19178v1 | 2403.19178 | 10.1007/s10115-024-02117-3 | 49 | 2 | false | null | Knowledge and Information Systems | 0.4247 |
bb6a99b66b6363ddb64e6e59ee0c6bfc7908e589dc846f94a4929b43ef46fd82 | [
"arxiv",
"semantic_scholar"
] | Enhancing Privacy in Federated Learning through Local Training | In this paper we propose the federated learning algorithm Fed-PLT to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coor... | [
"Nicola Bastianello",
"Changxin Liu",
"Karl H. Johansson"
] | [
"cs.LG",
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2024-03-26T00:00:00 | https://arxiv.org/abs/2403.17572 | https://arxiv.org/pdf/2403.17572v2 | 2403.17572 | 10.48550/arXiv.2403.17572 | 3 | 0 | false | null | arXiv.org | 0.1505 |
84164b1df5e29da04e275841fbc17f7ff190099c80507d5ed3da23c2ffdcaa9a | [
"arxiv",
"semantic_scholar"
] | The Privacy Policy Permission Model: A Unified View of Privacy Policies | Organizations use privacy policies to communicate their data collection practices to their clients. A privacy policy is a set of statements that specifies how an organization gathers, uses, discloses, and maintains a client's data. However, most privacy policies lack a clear, complete explanation of how data providers'... | [
"Maryam Majedi",
"Ken Barker"
] | [
"cs.CR",
"cs.CY"
] | [
"Computer Science"
] | 2024-03-26T00:00:00 | https://arxiv.org/abs/2403.17414 | https://arxiv.org/pdf/2403.17414v1 | 2403.17414 | 10.48550/arXiv.2403.17414 | 3 | 0 | false | null | Transactions on Data Privacy | 0.1505 |
6a888921ebf39cc060735d5c9d7e6b2f4631e2e702cb6465347e0a4f39acb9ac | [
"arxiv",
"semantic_scholar"
] | Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation | In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to ... | [
"Chengxi Li",
"Ming Xiao",
"Mikael Skoglund"
] | [
"eess.SP",
"cs.CR",
"cs.LG"
] | [
"Computer Science",
"Engineering"
] | 2024-03-22T00:00:00 | https://arxiv.org/abs/2403.14905 | https://arxiv.org/pdf/2403.14905v2 | 2403.14905 | 10.1109/TCOMM.2025.3594773 | 11 | 0 | false | null | IEEE Transactions on Communications | 0.2698 |
146239c06b15b57a62a2a1da36233ec01ea3f7bcc35222cca79a23138684b34d | [
"arxiv",
"semantic_scholar"
] | Improving LoRA in Privacy-preserving Federated Learning | Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency. LoRA injects a product of two trainable rank decomposition matrices over the top of each frozen pre-trained model modul... | [
"Youbang Sun",
"Zitao Li",
"Yaliang Li",
"Bolin Ding"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2024-03-18T00:00:00 | https://arxiv.org/abs/2403.12313 | https://arxiv.org/pdf/2403.12313v1 | 2403.12313 | 10.48550/arXiv.2403.12313 | 193 | 30 | false | null | International Conference on Learning Representations | 0.7457 |
c539d56dabee3b630e10f8bb9317d61b188d385f5e62808970a86597323e9d71 | [
"arxiv",
"semantic_scholar"
] | Federated Transfer Learning with Differential Privacy | Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by lev... | [
"Mengchu Li",
"Ye Tian",
"Yang Feng",
"Yi Yu"
] | [
"cs.LG",
"cs.CR",
"math.ST",
"stat.ME",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2024-03-17T00:00:00 | https://arxiv.org/abs/2403.11343 | https://arxiv.org/pdf/2403.11343v4 | 2403.11343 | 10.48550/arXiv.2403.11343 | 10 | 0 | false | null | arXiv.org | 0.2603 |
4156af5f8e4a74467f99a8ad7289c84af989ea1dfa4fa1bc8df335dceff4770f | [
"arxiv",
"semantic_scholar"
] | Federated Learning Method for Preserving Privacy in Face Recognition System | The state-of-the-art face recognition systems are typically trained on a single computer, utilizing extensive image datasets collected from various number of users. However, these datasets often contain sensitive personal information that users may hesitate to disclose. To address potential privacy concerns, we explore... | [
"Enoch Solomon",
"Abraham Woubie"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-03-08T00:00:00 | https://arxiv.org/abs/2403.05344 | https://arxiv.org/pdf/2403.05344v1 | 2403.05344 | 10.48550/arXiv.2403.05344 | 12 | 1 | false | null | arXiv.org | 0.2785 |
38586accf225602256ef76e8bac601f05814f655007136f45774d07bb9e66f88 | [
"arxiv",
"semantic_scholar"
] | Decoupled Subgraph Federated Learning | We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where interconnections between different clients play a critical role. We present a novel framework for this scenario, named FedStruc... | [
"Javad Aliakbari",
"Johan Östman",
"Alexandre Graell i Amat"
] | [
"cs.LG",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2402.19163 | https://arxiv.org/pdf/2402.19163v3 | 2402.19163 | null | 3 | 0 | false | null | International Conference on Learning Representations | 0.1505 |
6d636eaf57e98734768e8e6a9f32b5096321e9ec0ca1a1fb56fcc140697afbe7 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Distributed Optimization and Learning | Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which... | [
"Ziqin Chen",
"Yongqiang Wang"
] | [
"cs.LG",
"cs.CR",
"cs.GT"
] | [
"Computer Science"
] | 2024-02-29T00:00:00 | https://arxiv.org/abs/2403.00157 | https://arxiv.org/pdf/2403.00157v1 | 2403.00157 | 10.48550/arXiv.2403.00157 | 7 | 1 | false | null | arXiv.org | 0.2258 |
f854d711d02bc7c6dcc4c519e62513d39b9094f7bb0c873300b27273a2a0d23d | [
"arxiv",
"semantic_scholar"
] | A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research | Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that o... | [
"Jose L. Salmeron",
"Irina Arévalo"
] | [
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-02-15T00:00:00 | https://arxiv.org/abs/2402.10102 | https://arxiv.org/pdf/2402.10102v2 | 2402.10102 | 10.1007/978-3-030-52705-1_35 | 11 | 0 | false | null | null | 0.2698 |
a5abd3a9d385cd33f0ba7447575f418e0076ee38c539906b71974ba8d3ae6c36 | [
"arxiv",
"semantic_scholar"
] | Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning | In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. Group fairness refers to the principle that a model's decisions should be equitable across different groups defined by sensiti... | [
"Teresa Salazar",
"João Gama",
"Helder Araújo",
"Pedro Henriques Abreu"
] | [
"cs.LG"
] | [
"Computer Science",
"Medicine"
] | 2024-02-12T00:00:00 | https://arxiv.org/abs/2402.07586 | https://arxiv.org/pdf/2402.07586v4 | 2402.07586 | 10.1109/TNNLS.2025.3601834 | 10 | 0 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.2603 |
a9cb3f821694f6f8d7dd155a74fbb3b2e97a6e4aac6ae54feb387b35897c184c | [
"arxiv",
"semantic_scholar"
] | Federated Learning with Differential Privacy | Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Nevertheless, private information can still be divulged by analyzing uploaded parameter weights from clients. In this report, we showcase our empiri... | [
"Adrien Banse",
"Jan Kreischer",
"Xavier Oliva i Jürgens"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2024-02-03T00:00:00 | https://arxiv.org/abs/2402.02230 | https://arxiv.org/pdf/2402.02230v1 | 2402.02230 | 10.48550/arXiv.2402.02230 | 13 | 0 | false | null | arXiv.org | 0.2865 |
055b569b679ac07bf73da294aa8de64996279fa645bbe5a7a9eeda5bbb50170e | [
"arxiv",
"semantic_scholar"
] | Survey of Privacy Threats and Countermeasures in Federated Learning | Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to privacy in federated learning, and privacy countermeasures have been studied. However, we note that common and unique privacy threats among ... | [
"Masahiro Hayashitani",
"Junki Mori",
"Isamu Teranishi"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2024-02-01T00:00:00 | https://arxiv.org/abs/2402.00342 | https://arxiv.org/pdf/2402.00342v2 | 2402.00342 | 10.1109/FLTA67013.2025.11336767 | 2 | 0 | false | null | null | 0.1193 |
0e1504a61a7d4c12ef6b8ab967e38f0f61247824bb13096df97e409ad3649efb | [
"arxiv",
"semantic_scholar"
] | Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network | Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while pr... | [
"Qiong Wu",
"Wenhua Wang",
"Pingyi Fan",
"Qiang Fan",
"Huiling Zhu",
"Khaled B. Letaief"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2024-01-18T00:00:00 | https://arxiv.org/abs/2401.09886 | https://arxiv.org/pdf/2401.09886v2 | 2401.09886 | 10.1109/TNSM.2024.3403842 | 65 | 1 | true | https://github.com/qiongwu86/Edge-Caching-Based-on-Multi-Agent-Deep-Reinforcement-Learning-and-Federated-Learning | IEEE Transactions on Network and Service Management | 0.4549 |
1af0802819851e601316f9a40a52d00b30503c155262c87412d36705d5c68970 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving in Blockchain-based Federated Learning Systems | Federated Learning (FL) has recently arisen as a revolutionary approach to collaborative training Machine Learning models. According to this novel framework, multiple participants train a global model collaboratively, coordinating with a central aggregator without sharing their local data. As FL gains popularity in div... | [
"Sameera K. M.",
"Serena Nicolazzo",
"Marco Arazzi",
"Antonino Nocera",
"Rafidha Rehiman K. A.",
"Vinod P",
"Mauro Conti"
] | [
"cs.CR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-01-07T00:00:00 | https://arxiv.org/abs/2401.03552 | https://arxiv.org/pdf/2401.03552v1 | 2401.03552 | 10.1016/j.comcom.2024.04.024 | 81 | 1 | false | null | Computer Communications | 0.4785 |
7ba75a6a9a4e207c0f57357a842e0bbd7677bd8ac727d255f03330199074c6f7 | [
"arxiv",
"semantic_scholar"
] | Federated learning-outcome prediction with multi-layer privacy protection | Learning-outcome prediction (LOP) is a long-standing and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a di... | [
"Yupei Zhang",
"Yuxin Li",
"Yifei Wang",
"Shuangshuang Wei",
"Yunan Xu",
"Xuequn Shang"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2023-12-25T00:00:00 | https://arxiv.org/abs/2312.15608 | https://arxiv.org/pdf/2312.15608v1 | 2312.15608 | 10.1007/s11704-023-2791-8 | 16 | 0 | false | null | Frontiers of Computer Science, 2024,18(6):186604 | 0.3076 |
20098da7629044d3d718bf363b880a0745f498b34e4e703fd3ae5faf6b76fbba | [
"arxiv",
"semantic_scholar"
] | An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms | In today's world, the rapid expansion of IoT networks and the proliferation of smart devices in our daily lives, have resulted in the generation of substantial amounts of heterogeneous data. These data forms a stream which requires special handling. To handle this data effectively, advanced data processing technologies... | [
"Sofia Zahri",
"Hajar Bennouri",
"Ahmed M. Abdelmoniem"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2023-12-24T00:00:00 | https://arxiv.org/abs/2312.15375 | https://arxiv.org/pdf/2312.15375v1 | 2312.15375 | 10.48550/arXiv.2312.15375 | 6 | 2 | false | null | arXiv.org | 0.2386 |
16fabfeea49ad840edd2b4bba85aee2449d83f8842ddc111a260e95662346dc3 | [
"arxiv",
"semantic_scholar"
] | Federated learning with differential privacy and an untrusted aggregator | Federated learning for training models over mobile devices is gaining popularity. Current systems for this task exhibit significant trade-offs between model accuracy, privacy guarantee, and device efficiency. For instance, Oort (OSDI 2021) provides excellent accuracy and efficiency but requires a trusted central server... | [
"Kunlong Liu",
"Trinabh Gupta"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2023-12-17T00:00:00 | https://arxiv.org/abs/2312.10789 | https://arxiv.org/pdf/2312.10789v2 | 2312.10789 | 10.5220/0012322100003648 | 5 | 1 | false | null | International Conference on Information Systems Security and Privacy | 0.1945 |
c700c3c50c86df502e14a435af64cf1584bc8d79acfa3af837f687a50bc2a982 | [
"arxiv",
"semantic_scholar"
] | PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning | Federated learning (FL) has attracted growing attention since it allows for privacy-preserving collaborative training on decentralized clients without explicitly uploading sensitive data to the central server. However, recent works have revealed that it still has the risk of exposing private data to adversaries. In thi... | [
"Yuting Ma",
"Yuanzhi Yao",
"Xiaohua Xu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-12-16T00:00:00 | https://arxiv.org/abs/2312.10380 | https://arxiv.org/pdf/2312.10380v1 | 2312.10380 | 10.48550/arXiv.2312.10380 | 9 | 2 | true | https://github.com/ytingma/PPIDSG | AAAI Conference on Artificial Intelligence | 0.25 |
9ba7e2d14fe7c42875e6f67a6e150da8dcb17796f3c60a901d4f568079c69176 | [
"arxiv",
"semantic_scholar"
] | A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease | In the era of rapidly advancing medical technologies, the segmentation of medical data has become inevitable, necessitating the development of privacy preserving machine learning algorithms that can train on distributed data. Consolidating sensitive medical data is not always an option particularly due to the stringent... | [
"Paul K. Mandal"
] | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.DC"
] | [
"Computer Science"
] | 2023-12-15T00:00:00 | https://arxiv.org/abs/2312.10237 | https://arxiv.org/pdf/2312.10237v5 | 2312.10237 | 10.1007/s00521-024-10419-4 | 8 | 1 | false | null | Neural Comput & Applic (2024) | 0.2386 |
f013143a69ff701be375438761a4aed25da0adc5151673a8a327846bcc011a40 | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving quantum federated learning via gradient hiding | Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes. Meanwhile, the critical concern of privacy within distributed c... | [
"Changhao Li",
"Niraj Kumar",
"Zhixin Song",
"Shouvanik Chakrabarti",
"Marco Pistoia"
] | [
"quant-ph",
"cs.CR",
"cs.DC",
"cs.LG"
] | [
"Physics",
"Computer Science"
] | 2023-12-07T00:00:00 | https://arxiv.org/abs/2312.04447 | https://arxiv.org/pdf/2312.04447v1 | 2312.04447 | 10.1088/2058-9565/ad40cc | 37 | 1 | false | null | Quantum Science and Technology | 0.3949 |
4ec3ed74c31415532f6781aac5e56a9af330933105f3041fdf87906440aafd76 | [
"arxiv",
"semantic_scholar"
] | Federated Learning is Better with Non-Homomorphic Encryption | Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choic... | [
"Konstantin Burlachenko",
"Abdulmajeed Alrowithi",
"Fahad Ali Albalawi",
"Peter Richtarik"
] | [
"cs.CR",
"cs.LG",
"math.OC"
] | [
"Computer Science",
"Mathematics"
] | 2023-12-04T00:00:00 | https://arxiv.org/abs/2312.02074 | https://arxiv.org/pdf/2312.02074v1 | 2312.02074 | 10.1145/3630048.3630182 | 6 | 0 | false | null | Proceedings of the 4th International Workshop on Distributed Machine Learning December 2023 | 0.2113 |
5890630a18334257c25a09ac9f00bfb6bcb341326132338d512e672c435718c8 | [
"arxiv",
"semantic_scholar"
] | Evaluating Multi-Global Server Architecture for Federated Learning | Federated learning (FL) with a single global server framework is currently a popular approach for training machine learning models on decentralized environment, such as mobile devices and edge devices. However, the centralized server architecture poses a risk as any challenge on the central/global server would result i... | [
"Asfia Kawnine",
"Hung Cao",
"Atah Nuh Mih",
"Monica Wachowicz"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2023-11-26T00:00:00 | https://arxiv.org/abs/2311.15382 | https://arxiv.org/pdf/2311.15382v1 | 2311.15382 | 10.1109/ICCE59016.2024.10444349 | 2 | 0 | false | null | IEEE International Conference on Consumer Electronics | 0.1193 |
6a2ea470b5025738d4e5fa71dedfa29cd8ac3cd88ac57b12ce091aad5a7c815b | [
"arxiv",
"semantic_scholar"
] | Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks | 5G and Beyond Networks become increasingly complex and heterogeneous, with diversified and high requirements from a wide variety of emerging applications. The complexity and diversity of Telecom networks place an increasing strain on maintenance and operation efforts. Moreover, the strict security and privacy requireme... | [
"R. Bourgerie",
"T. Zanouda"
] | [
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2023-11-24T00:00:00 | https://arxiv.org/abs/2311.14469 | https://arxiv.org/pdf/2311.14469v1 | 2311.14469 | 10.1109/ICDMW60847.2023.10449399 | 11 | 0 | false | null | null | 0.2698 |
91350df94f4adb41230fd340fe57ea85cdd92ab978356cbf143f59953f1e3c5c | [
"arxiv",
"semantic_scholar"
] | FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning | Federated Learning (FL) is a collaborative method for training models while preserving data privacy in decentralized settings. However, FL encounters challenges related to data heterogeneity, which can result in performance degradation. In our study, we observe that as data heterogeneity increases, feature representati... | [
"Seongyoon Kim",
"Gihun Lee",
"Jaehoon Oh",
"Se-Young Yun"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2023-11-22T00:00:00 | https://arxiv.org/abs/2311.13267 | https://arxiv.org/pdf/2311.13267v1 | 2311.13267 | 10.48550/arXiv.2311.13267 | 9 | 0 | false | null | arXiv.org | 0.25 |
bea9e0639827bd170a39d559e3dfebfd7fbc6a4f897fdd2b127e1576ecfe43d3 | [
"arxiv",
"semantic_scholar"
] | FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection | The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practic... | [
"Wenqing Wei",
"Zhengdong Yang",
"Yuan Gao",
"Jiyi Li",
"Chenhui Chu",
"Shogo Okada",
"Sheng Li"
] | [
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2023-11-21T00:00:00 | https://arxiv.org/abs/2311.13043 | https://arxiv.org/pdf/2311.13043v1 | 2311.13043 | 10.1109/ASRU57964.2023.10389690 | 3 | 0 | false | null | Automatic Speech Recognition & Understanding | 0.1505 |
a66a777cc3f4b3fd7f8654e37584260fd833ff34b3d4a5f322695e157b6b40e5 | [
"arxiv",
"semantic_scholar"
] | Contribution Evaluation in Federated Learning: Examining Current Approaches | Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, clients with private and potentially heterogeneous data and compute resources come together to train a common model without raw data ever lea... | [
"Vasilis Siomos",
"Jonathan Passerat-Palmbach"
] | [
"cs.LG",
"cs.DC",
"cs.GT"
] | [
"Computer Science"
] | 2023-11-16T00:00:00 | https://arxiv.org/abs/2311.09856 | https://arxiv.org/pdf/2311.09856v1 | 2311.09856 | 10.48550/arXiv.2311.09856 | 7 | 0 | false | null | arXiv.org | 0.2258 |
eff953ac2742cf2498c58a523153e9e9ccbe3b57ae5d54e8e3b1c6404953f5dc | [
"arxiv",
"semantic_scholar"
] | Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies | Federated learning (FL) is an emerging paradigm for training deep neural networks (DNNs) in distributed manners. Current FL approaches all suffer from high communication overhead and information leakage. In this work, we present a federated learning algorithm based on evolution strategies (FedES), a zeroth-order traini... | [
"Guangchen Lan"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-11-05T00:00:00 | https://arxiv.org/abs/2311.03405 | https://arxiv.org/pdf/2311.03405v2 | 2311.03405 | 10.48550/arXiv.2311.03405 | 0 | 0 | false | null | arXiv.org | 0 |
7fb443be7e1605927b06fb4e864882728f9b21bf74b5d34d79865aeca05ebde8 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection | The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks. Trust among these financial institutions is limited by regulation and competition. Federated learning (FL) enables entiti... | [
"Swanand Ravindra Kadhe",
"Heiko Ludwig",
"Nathalie Baracaldo",
"Alan King",
"Yi Zhou",
"Keith Houck",
"Ambrish Rawat",
"Mark Purcell",
"Naoise Holohan",
"Mikio Takeuchi",
"Ryo Kawahara",
"Nir Drucker",
"Hayim Shaul",
"Eyal Kushnir",
"Omri Soceanu"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2023-10-30T00:00:00 | https://arxiv.org/abs/2310.19304 | https://arxiv.org/pdf/2310.19304v1 | 2310.19304 | 10.48550/arXiv.2310.19304 | 5 | 0 | false | null | arXiv.org | 0.1945 |
a666151abb96ac610fdfeda8d26b1b7a548ce4f42caf34a8053383d4368a4fe1 | [
"arxiv",
"semantic_scholar"
] | Serverless Federated Learning with flwr-serverless | Federated learning is becoming increasingly relevant and popular as we witness a surge in data collection and storage of personally identifiable information. Alongside these developments there have been many proposals from governments around the world to provide more protections for individuals' data and a heightened i... | [
"Sanjeev V. Namjoshi",
"Reese Green",
"Krishi Sharma",
"Zhangzhang Si"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-10-23T00:00:00 | https://arxiv.org/abs/2310.15329 | https://arxiv.org/pdf/2310.15329v1 | 2310.15329 | 10.48550/arXiv.2310.15329 | 0 | 0 | true | null | arXiv.org | 0 |
736759510d64cdf972144e1f1185ebc05f1e97349c8cbef153d65d598f8c89a6 | [
"arxiv",
"semantic_scholar"
] | Federated Quantum Machine Learning with Differential Privacy | The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the no-cloning theorem, resulting in a most desirable computational platform on ... | [
"Rod Rofougaran",
"Shinjae Yoo",
"Huan-Hsin Tseng",
"Samuel Yen-Chi Chen"
] | [
"quant-ph",
"cs.LG"
] | [
"Physics",
"Computer Science"
] | 2023-10-10T00:00:00 | https://arxiv.org/abs/2310.06973 | https://arxiv.org/pdf/2310.06973v1 | 2310.06973 | 10.1109/ICASSP48485.2024.10447155 | 45 | 0 | false | null | IEEE International Conference on Acoustics, Speech, and Signal Processing | 0.4157 |
aa9f1424f8b4e9380f9d85db541a6902d3d6c503e1d865dd3942c9f39b91a1c5 | [
"arxiv",
"semantic_scholar"
] | FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler | Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralized data facility. Nonetheless, because of the disparity of computing re... | [
"Zilinghan Li",
"Pranshu Chaturvedi",
"Shilan He",
"Han Chen",
"Gagandeep Singh",
"Volodymyr Kindratenko",
"E. A. Huerta",
"Kibaek Kim",
"Ravi Madduri"
] | [
"cs.LG",
"cs.DC"
] | [
"Computer Science"
] | 2023-09-26T00:00:00 | https://arxiv.org/abs/2309.14675 | https://arxiv.org/pdf/2309.14675v2 | 2309.14675 | 10.48550/arXiv.2309.14675 | 23 | 1 | true | https://github.com/APPFL/FedCompass | International Conference on Learning Representations | 0.3451 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.