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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
efdaaa3d345d01dd26341c4f690aebf5287b6fe857f02ea16b139b46114a147f | [
"arxiv",
"semantic_scholar"
] | Towards Energy-Aware Federated Traffic Prediction for Cellular Networks | Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine learning (ML) is a promising approach to effectively predict network t... | [
"Vasileios Perifanis",
"Nikolaos Pavlidis",
"Selim F. Yilmaz",
"Francesc Wilhelmi",
"Elia Guerra",
"Marco Miozzo",
"Pavlos S. Efraimidis",
"Paolo Dini",
"Remous-Aris Koutsiamanis"
] | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.NI"
] | [
"Computer Science"
] | 2023-09-19T00:00:00 | https://arxiv.org/abs/2309.10645 | https://arxiv.org/pdf/2309.10645v1 | 2309.10645 | 10.1109/FMEC59375.2023.10306017 | 18 | 2 | false | null | International Conference on Fog and Mobile Edge Computing | 0.3197 |
ba790567edca696b1eaf88f74edea291fc6bcf05fd4df99a54621082ed7284b5 | [
"arxiv",
"semantic_scholar"
] | Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks | Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed to prevent gradient leakage without loss of model utility by incorporating a PRivacy EnhanCing mODulE (PRECODE) based on variational modeli... | [
"Daniel Scheliga",
"Patrick Mäder",
"Marco Seeland"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2023-09-08T00:00:00 | https://arxiv.org/abs/2309.04515 | https://arxiv.org/pdf/2309.04515v1 | 2309.04515 | 10.1186/s42400-024-00295-9 | 11 | 1 | false | null | Cybersecurity | 0.2698 |
828d74dc22ab3093f253d1b3d212b3b6a5ee44e711118c9d95d774b2f2f037e7 | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory | With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserv... | [
"Naoki Masuyama",
"Yusuke Nojima",
"Yuichiro Toda",
"Chu Kiong Loo",
"Hisao Ishibuchi",
"Naoyuki Kubota"
] | [
"cs.LG",
"cs.CR",
"cs.NE"
] | [
"Computer Science"
] | 2023-09-07T00:00:00 | https://arxiv.org/abs/2309.03487 | https://arxiv.org/pdf/2309.03487v1 | 2309.03487 | 10.1109/ACCESS.2024.3467114 | 7 | 0 | true | https://github.com/Masuyama-lab/FCAC} | IEEE Access | 0.2258 |
af8720880177983dc50995445eea8aea40d1d9e9d7a7e5a3413e8de48336eea4 | [
"arxiv",
"semantic_scholar"
] | Bias Propagation in Federated Learning | We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive attributes such as gender or race) can propagate through the network to all the parties in the network. We analyze and explain bias propagat... | [
"Hongyan Chang",
"Reza Shokri"
] | [
"cs.LG",
"cs.CY",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-09-05T00:00:00 | https://arxiv.org/abs/2309.02160 | https://arxiv.org/pdf/2309.02160v1 | 2309.02160 | 10.48550/arXiv.2309.02160 | 24 | 4 | false | null | International Conference on Learning Representations | 0.3495 |
a926787aa57d259a3d216bada16613eaeb7a07dc00153862bfdb68bd967f5433 | [
"arxiv",
"semantic_scholar"
] | FedFwd: Federated Learning without Backpropagation | In federated learning (FL), clients with limited resources can disrupt the training efficiency. A potential solution to this problem is to leverage a new learning procedure that does not rely on backpropagation (BP). We present a novel approach to FL called FedFwd that employs a recent BP-free method by Hinton (2022), ... | [
"Seonghwan Park",
"Dahun Shin",
"Jinseok Chung",
"Namhoon Lee"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2023-09-03T00:00:00 | https://arxiv.org/abs/2309.01150 | https://arxiv.org/pdf/2309.01150v1 | 2309.01150 | 10.48550/arXiv.2309.01150 | 6 | 3 | false | null | arXiv.org | 0.301 |
99bf45a5a1bb45596338ffc4db97966bd6cba619e83bf65a5757023bf22a888d | [
"arxiv",
"semantic_scholar"
] | Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond | Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minimizing a cost function over their local inputs. FL was proposed as a st... | [
"Filippo Galli",
"Kangsoo Jung",
"Sayan Biswas",
"Catuscia Palamidessi",
"Tommaso Cucinotta"
] | [
"cs.LG",
"cs.CR",
"cs.CY",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-09-01T00:00:00 | https://arxiv.org/abs/2309.00416 | https://arxiv.org/pdf/2309.00416v1 | 2309.00416 | 10.1007/s42979-023-02292-0 | 17 | 1 | false | null | SN Computer Science | 0.3138 |
4a8c9b298ddebc78a3ea56e0d8a21b0408d6ea22446bdd3e81e0c7682a973ee2 | [
"arxiv",
"semantic_scholar"
] | APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service | Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that pro... | [
"Zilinghan Li",
"Shilan He",
"Pranshu Chaturvedi",
"Trung-Hieu Hoang",
"Minseok Ryu",
"E. A. Huerta",
"Volodymyr Kindratenko",
"Jordan Fuhrman",
"Maryellen Giger",
"Ryan Chard",
"Kibaek Kim",
"Ravi Madduri"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-08-17T00:00:00 | https://arxiv.org/abs/2308.08786 | https://arxiv.org/pdf/2308.08786v1 | 2308.08786 | 10.1109/e-Science58273.2023.10254842 | 17 | 0 | false | null | IEEE International Conference on e-Science | 0.3138 |
50c53b4bacd78b7512be8af45de1c6633bb7bb8d0fcbe450214767cd21a2dc02 | [
"arxiv",
"semantic_scholar"
] | Binary Federated Learning with Client-Level Differential Privacy | Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm and implement differential privacy with a Gaussian mechanism. Howe... | [
"Lumin Liu",
"Jun Zhang",
"Shenghui Song",
"Khaled B. Letaief"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2023-08-07T00:00:00 | https://arxiv.org/abs/2308.03320 | https://arxiv.org/pdf/2308.03320v1 | 2308.03320 | 10.1109/GLOBECOM54140.2023.10437593 | 4 | 0 | false | null | Global Communications Conference | 0.1747 |
585d647648020d5ed2055ba52370ff865d63581194a6978423df650df553364c | [
"arxiv",
"semantic_scholar"
] | Towards Fair and Privacy Preserving Federated Learning for the Healthcare Domain | Federated learning enables data sharing in healthcare contexts where it might otherwise be difficult due to data-use-ordinances or security and communication constraints. Distributed and shared data models allow models to become generalizable and learn from heterogeneous clients. While addressing data security, privacy... | [
"Navya Annapareddy",
"Yingzheng Liu",
"Judy Fox"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2023-08-03T00:00:00 | https://arxiv.org/abs/2308.01529 | https://arxiv.org/pdf/2308.01529v1 | 2308.01529 | 10.48550/arXiv.2308.01529 | 4 | 0 | false | null | arXiv.org | 0.1747 |
964f307fc08223c49d0b6edafa72a91ca73d8099fe0cb9c4510325f97f1d454c | [
"arxiv",
"semantic_scholar"
] | Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation | Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability. However, it has a high communication cost, which has not been examined with quantized communications. To tackle this problem, we present a new algorithm (QAFeL), with a quan... | [
"Tomas Ortega",
"Hamid Jafarkhani"
] | [
"cs.LG",
"eess.SP",
"math.OC"
] | [
"Computer Science",
"Engineering",
"Mathematics"
] | 2023-08-01T00:00:00 | https://arxiv.org/abs/2308.00263 | https://arxiv.org/pdf/2308.00263v1 | 2308.00263 | 10.48550/arXiv.2308.00263 | 12 | 0 | false | null | arXiv.org | 0.2785 |
7c3dbdd9b72645ce913794611d9e75b517a2ae98634e3a43088998b6da290592 | [
"arxiv",
"semantic_scholar"
] | Federated Learning for Data and Model Heterogeneity in Medical Imaging | Federated Learning (FL) is an evolving machine learning method in which multiple clients participate in collaborative learning without sharing their data with each other and the central server. In real-world applications such as hospitals and industries, FL counters the challenges of data heterogeneity and model hetero... | [
"Hussain Ahmad Madni",
"Rao Muhammad Umer",
"Gian Luca Foresti"
] | [
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2023-07-31T00:00:00 | https://arxiv.org/abs/2308.00155 | https://arxiv.org/pdf/2308.00155v1 | 2308.00155 | 10.48550/arXiv.2308.00155 | 9 | 0 | false | null | null | 0.25 |
58ed0faf6ba73e18355b6d1ec47829188d7e3c2d88fa52a0e9cf9b68318c181d | [
"arxiv",
"semantic_scholar"
] | Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning | Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typ... | [
"Attia Qammar",
"Abdenacer Naouri",
"Jianguo Ding",
"Huansheng Ning"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2023-07-25T00:00:00 | https://arxiv.org/abs/2308.04442 | https://arxiv.org/pdf/2308.04442v1 | 2308.04442 | 10.48550/arXiv.2308.04442 | 2 | 0 | false | null | arXiv.org | 0.1193 |
11a075fe7b4d2c48fa5c63c88282924d534246b8912bd71c4f62ccefcf09ef49 | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving patient clustering for personalized federated learning | Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical sett... | [
"Ahmed Elhussein",
"Gamze Gursoy"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science",
"Medicine"
] | 2023-07-17T00:00:00 | https://arxiv.org/abs/2307.08847 | https://arxiv.org/pdf/2307.08847v1 | 2307.08847 | 10.48550/arXiv.2307.08847 | 16 | 1 | false | null | Machine Learning in Health Care | 0.3076 |
2556472dbd867cc44648919221d60b0b2a563818e944f9df2b137dbac75e922c | [
"arxiv",
"semantic_scholar"
] | FDAPT: Federated Domain-adaptive Pre-training for Language Models | Foundation models (FMs) have shown prominent success in a wide range of tasks. Their applicability to specific domain-task pairings relies on the availability of, both, high-quality data and significant computational resources. These challenges are not new to the field and, indeed, Federated Learning (FL) has been show... | [
"Lekang Jiang",
"Filip Svoboda",
"Nicholas D. Lane"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2023-07-12T00:00:00 | https://arxiv.org/abs/2307.06933 | https://arxiv.org/pdf/2307.06933v2 | 2307.06933 | 10.48550/arXiv.2307.06933 | 5 | 0 | false | null | arXiv.org | 0.1945 |
8c345c14c80628295a994b7190dc622488194778aca810587a8ee56955bfab96 | [
"arxiv",
"semantic_scholar"
] | Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning | The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data which protects the privacy of individuals has proven challenging. Despite progress in differential privacy and generative modeling for privac... | [
"Tamas Madl",
"Weijie Xu",
"Olivia Choudhury",
"Matthew Howard"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2023-07-04T00:00:00 | https://arxiv.org/abs/2307.01875 | https://arxiv.org/pdf/2307.01875v1 | 2307.01875 | 10.48550/arXiv.2307.01875 | 8 | 0 | false | null | arXiv.org | 0.2386 |
c2a64a821807bd7c3d55256d4ff914a0b160af196a03e03e19df8049ad0b384b | [
"arxiv",
"semantic_scholar"
] | Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification | The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in na... | [
"Kishore Babu Nampalle",
"Pradeep Singh",
"Uppala Vivek Narayan",
"Balasubramanian Raman"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2023-06-30T00:00:00 | https://arxiv.org/abs/2306.17794 | https://arxiv.org/pdf/2306.17794v1 | 2306.17794 | 10.48550/arXiv.2306.17794 | 15 | 1 | false | null | arXiv.org | 0.301 |
a9cfb300935a5fc8a1aed464839dfbb0a18489a1a4d35d5aa13bf451665792b3 | [
"arxiv",
"semantic_scholar"
] | A Survey on Blockchain-Based Federated Learning and Data Privacy | Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distribu... | [
"Bipin Chhetri",
"Saroj Gopali",
"Rukayat Olapojoye",
"Samin Dehbash",
"Akbar Siami Namin"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-06-29T00:00:00 | https://arxiv.org/abs/2306.17338 | https://arxiv.org/pdf/2306.17338v1 | 2306.17338 | 10.1109/COMPSAC57700.2023.00199 | 26 | 1 | false | null | Annual International Computer Software and Applications Conference | 0.3578 |
18fd72f9227df3c45b244ebc7c19e6ab8a2d323c8c347455cb1d6b4aa3335226 | [
"arxiv",
"semantic_scholar"
] | Differentially Private Distributed Estimation and Learning | We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estimate the unknown quantities by exchanging information about their priva... | [
"Marios Papachristou",
"M. Amin Rahimian"
] | [
"cs.LG",
"cs.SI",
"eess.SY",
"math.ST",
"stat.AP",
"stat.ML"
] | [
"Computer Science",
"Engineering",
"Mathematics"
] | 2023-06-28T00:00:00 | https://arxiv.org/abs/2306.15865 | https://arxiv.org/pdf/2306.15865v5 | 2306.15865 | 10.1080/24725854.2024.2337068 | 2 | 0 | false | null | IISE Transactions | 0.1193 |
59463ac508e14ef1cd259dc693f9f73c1e60bb8eb76b8d147eac02af412247fd | [
"arxiv",
"semantic_scholar"
] | Medical Federated Model with Mixture of Personalized and Sharing Components | Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical... | [
"Yawei Zhao",
"Qinghe Liu",
"Xinwang Liu",
"Kunlun He"
] | [
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-06-26T00:00:00 | https://arxiv.org/abs/2306.14483 | https://arxiv.org/pdf/2306.14483v1 | 2306.14483 | 10.48550/arXiv.2306.14483 | 5 | 0 | true | https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code | arXiv.org | 0.1945 |
6add2169a6291f32554c290c1fb98105478281303b76382f9daf4e306cf23e9f | [
"arxiv",
"semantic_scholar"
] | Towards Quantum Federated Learning | Quantum Federated Learning (QFL) is an emerging interdisciplinary field that merges the principles of Quantum Computing (QC) and Federated Learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for... | [
"Chao Ren",
"Rudai Yan",
"Huihui Zhu",
"Han Yu",
"Minrui Xu",
"Yuan Shen",
"Yan Xu",
"Ming Xiao",
"Zhao Yang Dong",
"Mikael Skoglund",
"Dusit Niyato",
"Leong Chuan Kwek"
] | [
"cs.LG",
"quant-ph"
] | [
"Computer Science",
"Physics"
] | 2023-06-16T00:00:00 | https://arxiv.org/abs/2306.09912 | https://arxiv.org/pdf/2306.09912v4 | 2306.09912 | 10.48550/arXiv.2306.09912 | 35 | 2 | false | null | arXiv.org | 0.3891 |
8f4414860074f5a3b36486d68e290fa03557d8e80c53fe4bae59d03de544aa6d | [
"arxiv",
"semantic_scholar"
] | Fairness and Privacy-Preserving in Federated Learning: A Survey | Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized training of decentralized edge clients on their own private data. The existin... | [
"Taki Hasan Rafi",
"Faiza Anan Noor",
"Tahmid Hussain",
"Dong-Kyu Chae"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2023-06-14T00:00:00 | https://arxiv.org/abs/2306.08402 | https://arxiv.org/pdf/2306.08402v2 | 2306.08402 | 10.48550/arXiv.2306.08402 | 98 | 2 | false | null | Information Fusion | 0.4989 |
7a30398a38b3e0eb6a4f26efe1a5113af9535755d7684f12a9c9699498338a19 | [
"arxiv",
"semantic_scholar"
] | Personalized Graph Federated Learning with Differential Privacy | This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among diff... | [
"Francois Gauthier",
"Vinay Chakravarthi Gogineni",
"Stefan Werner",
"Yih-Fang Huang",
"Anthony Kuh"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-06-10T00:00:00 | https://arxiv.org/abs/2306.06399 | https://arxiv.org/pdf/2306.06399v1 | 2306.06399 | 10.1109/TSIPN.2023.3325963 | 18 | 1 | false | null | IEEE Transactions on Signal and Information Processing over Networks | 0.3197 |
66bbdaf38cf3864fec49d297eddc65a730db140aec0c41a0b844d05caf0bfb57 | [
"arxiv",
"semantic_scholar"
] | A Privacy-Preserving Federated Learning Approach for Kernel methods | It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise. An example for such a use case is machine learning on clinical ... | [
"Anika Hannemann",
"Ali Burak Ünal",
"Arjhun Swaminathan",
"Erik Buchmann",
"Mete Akgün"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2023-06-05T00:00:00 | https://arxiv.org/abs/2306.02677 | https://arxiv.org/pdf/2306.02677v1 | 2306.02677 | 10.1109/TPS-ISA58951.2023.00020 | 7 | 0 | false | null | International Conference on Trust, Privacy and Security in Intelligent Systems and Applications | 0.2258 |
61f95cd49e4c25329346847baa5e4c5a790c0a6da6d71fdb32c7b3230fab84cb | [
"arxiv",
"semantic_scholar"
] | Forgettable Federated Linear Learning with Certified Data Unlearning | Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address the "right to be forgotten" and to remove the influence of poisoned or target clients without retraining the entire FL system. However, many F... | [
"Ruinan Jin",
"Minghui Chen",
"Qiong Zhang",
"Xiaoxiao Li"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2023-06-03T00:00:00 | https://arxiv.org/abs/2306.02216 | https://arxiv.org/pdf/2306.02216v3 | 2306.02216 | 10.1109/TNNLS.2026.3683398 | 7 | 0 | true | https://github.com/Nanboy-Ronan/2F2L-Federated-Unlearning | IEEE Transactions on Neural Networks and Learning Systems | 0.2258 |
d8f28064ac1a79646341d4ba517c02ecfbef6526035bcfb40bc12f756877e3a5 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Model Aggregation for Asynchronous Federated Learning | We present a novel privacy-preserving model aggregation for asynchronous federated learning, named PPA-AFL that removes the restriction of synchronous aggregation of local model updates in federated learning, while enabling the protection of the local model updates against the server. In PPA-AFL, clients can proactive ... | [
"Jianxiang Zhao",
"Xiangman Li",
"Jianbing Ni"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2023-05-27T00:00:00 | https://arxiv.org/abs/2305.17521 | https://arxiv.org/pdf/2305.17521v1 | 2305.17521 | 10.1109/ICCC57788.2023.10233295 | 2 | 1 | false | null | null | 0.1505 |
001c06c586e8f9b9af0d5970a1eb983f9f85f3f4b716f2f254412f1b45af60f9 | [
"arxiv",
"semantic_scholar"
] | Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models | Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training. However, optimizing FMs often requires access to sensitive data, raising privacy concerns and limiting their ... | [
"Sixing Yu",
"J. Pablo Muñoz",
"Ali Jannesari"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2023-05-19T00:00:00 | https://arxiv.org/abs/2305.11414 | https://arxiv.org/pdf/2305.11414v3 | 2305.11414 | 10.48550/arXiv.2305.11414 | 76 | 3 | false | null | International Conference on Language Resources and Evaluation | 0.4716 |
939d21badd6715c3328e933bfae3e4f0de4698901bb1eedd55c5f1ef80a74392 | [
"arxiv",
"semantic_scholar"
] | Efficient Vertical Federated Learning with Secure Aggregation | The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently. However, in many interesting problems, such as financial fraud detection and disease detection, individual ... | [
"Xinchi Qiu",
"Heng Pan",
"Wanru Zhao",
"Chenyang Ma",
"Pedro Porto Buarque de Gusmão",
"Nicholas D. Lane"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2023-05-18T00:00:00 | https://arxiv.org/abs/2305.11236 | https://arxiv.org/pdf/2305.11236v1 | 2305.11236 | 10.48550/arXiv.2305.11236 | 6 | 0 | false | null | arXiv.org | 0.2113 |
4dbeedd6e5b4a89c200ca4afad9b337ed039a4ee6d92cc4f67d1850032837765 | [
"arxiv",
"semantic_scholar"
] | MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning | With the growth of computer vision applications, deep learning, and edge computing contribute to ensuring practical collaborative intelligence (CI) by distributing the workload among edge devices and the cloud. However, running separate single-task models on edge devices is inefficient regarding the required computatio... | [
"Md Adnan Arefeen",
"Zhouyu Li",
"Md Yusuf Sarwar Uddin",
"Anupam Das"
] | [
"cs.CV",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2023-05-13T00:00:00 | https://arxiv.org/abs/2305.07815 | https://arxiv.org/pdf/2305.07815v1 | 2305.07815 | 10.1145/3576842.3582372 | 0 | 0 | false | null | International Conference on Internet-of-Things Design and Implementation | 0 |
0967366e706f3b5d298bbf3b993d9712056e35c2694153b09fc7d79cbff7d4f7 | [
"arxiv",
"semantic_scholar"
] | MISO: Legacy-compatible Privacy-preserving Single Sign-on using Trusted Execution Environments | Single sign-on (SSO) allows users to authenticate to third-party applications through a central identity provider. Despite their wide adoption, deployed SSO systems suffer from privacy problems such as user tracking by the identity provider. While numerous solutions have been proposed by academic papers, none were adop... | [
"Rongwu Xu",
"Sen Yang",
"Fan Zhang",
"Zhixuan Fang"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2023-05-11T00:00:00 | https://arxiv.org/abs/2305.06833 | https://arxiv.org/pdf/2305.06833v2 | 2305.06833 | 10.1109/EuroSP57164.2023.00029 | 10 | 2 | false | null | European Symposium on Security and Privacy | 0.2603 |
8517271064f55685bb5c11b69b3e8f784df34b69e7a669b1c6f3a7f12b9c136b | [
"arxiv",
"semantic_scholar"
] | Turning Privacy-preserving Mechanisms against Federated Learning | Recently, researchers have successfully employed Graph Neural Networks (GNNs) to build enhanced recommender systems due to their capability to learn patterns from the interaction between involved entities. In addition, previous studies have investigated federated learning as the main solution to enable a native privacy... | [
"Marco Arazzi",
"Mauro Conti",
"Antonino Nocera",
"Stjepan Picek"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2023-05-09T00:00:00 | https://arxiv.org/abs/2305.05355 | https://arxiv.org/pdf/2305.05355v1 | 2305.05355 | 10.1145/3576915.3623114 | 21 | 1 | false | null | Conference on Computer and Communications Security | 0.3356 |
5074a7bd72b62b56873696c5861542c9b3b223ea31583ad30a9a13c9077dc539 | [
"arxiv",
"semantic_scholar"
] | Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion | Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the requirements in preserving \textit{privacy} and maintaining high model \textit{utility}. The... | [
"Xiaojin Zhang",
"Kai Chen",
"Qiang Yang"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2023-05-07T00:00:00 | https://arxiv.org/abs/2305.04288 | https://arxiv.org/pdf/2305.04288v3 | 2305.04288 | 10.48550/arXiv.2305.04288 | 6 | 0 | false | null | arXiv.org | 0.2113 |
4b4e0d82821f877625bacba7dd3c58e394ab5cedf4bf009a12aeea6609d39b6f | [
"arxiv",
"semantic_scholar"
] | Personalized Federated Learning under Mixture of Distributions | The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy. However, current PFL techniques primarily focus on modeling the conditional distribution heterogeneity (i.e. concept ... | [
"Yue Wu",
"Shuaicheng Zhang",
"Wenchao Yu",
"Yanchi Liu",
"Quanquan Gu",
"Dawei Zhou",
"Haifeng Chen",
"Wei Cheng"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-05-01T00:00:00 | https://arxiv.org/abs/2305.01068 | https://arxiv.org/pdf/2305.01068v1 | 2305.01068 | 10.48550/arXiv.2305.01068 | 73 | 3 | false | null | International Conference on Machine Learning | 0.4673 |
5c06095ccbd931c0d5c5e3e18fc17e634cc926f21e3bbe5ac4fe36a77d25b79a | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression | In this paper, we present a practical solution to implement privacy-preserving CNN training based on mere Homomorphic Encryption (HE) technique. To our best knowledge, this is the first attempt successfully to crack this nut and no work ever before has achieved this goal. Several techniques combine to accomplish the ta... | [
"John Chiang"
] | [
"cs.CR",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2023-04-07T00:00:00 | https://arxiv.org/abs/2304.03807 | https://arxiv.org/pdf/2304.03807v5 | 2304.03807 | 10.48550/arXiv.2304.03807 | 8 | 0 | true | https://github.com/petitioner/HE.CNNtraining}{$\texttt{https://github.com/petitioner/HE.CNNtraining}$} | arXiv.org | 0.2386 |
214c60fa22a21a9abc34e66c41351bcac635672c1b4fe76d347060fb3d13bc91 | [
"arxiv",
"semantic_scholar"
] | FedBot: Enhancing Privacy in Chatbots with Federated Learning | Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as d... | [
"Addi Ait-Mlouk",
"Sadi Alawadi",
"Salman Toor",
"Andreas Hellander"
] | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2023-04-04T00:00:00 | https://arxiv.org/abs/2304.03228 | https://arxiv.org/pdf/2304.03228v1 | 2304.03228 | 10.48550/arXiv.2304.03228 | 8 | 1 | false | null | arXiv.org | 0.2386 |
53e8bfe898b34045a43d413c33508d538277703508d1f90e34186be71c980a74 | [
"arxiv",
"semantic_scholar"
] | Scalable and Privacy-Preserving Federated Principal Component Analysis | Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confidentiality. Our solution, SF-PCA, is an end-to-end secure system... | [
"David Froelicher",
"Hyunghoon Cho",
"Manaswitha Edupalli",
"Joao Sa Sousa",
"Jean-Philippe Bossuat",
"Apostolos Pyrgelis",
"Juan R. Troncoso-Pastoriza",
"Bonnie Berger",
"Jean-Pierre Hubaux"
] | [
"cs.CR"
] | [
"Computer Science",
"Medicine"
] | 2023-03-31T00:00:00 | https://arxiv.org/abs/2304.00129 | https://arxiv.org/pdf/2304.00129v1 | 2304.00129 | 10.1109/SP46215.2023.00051 | 30 | 3 | false | null | IEEE Symposium on Security and Privacy | 0.3728 |
e28750862dc20224f1520a8f6f9e7cd55120e90e37588aecef3cff78d53c3dc6 | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving machine learning for healthcare: open challenges and future perspectives | Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this ... | [
"Alejandro Guerra-Manzanares",
"L. Julian Lechuga Lopez",
"Michail Maniatakos",
"Farah E. Shamout"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2023-03-27T00:00:00 | https://arxiv.org/abs/2303.15563 | https://arxiv.org/pdf/2303.15563v1 | 2303.15563 | 10.1007/978-3-031-39539-0_3 | 25 | 3 | false | null | Trustworthy Machine Learning for Healthcare. TML4H 2023. Lecture Notes in Computer Science, vol 13932 | 0.3537 |
234a55461e74a03cdd24cf7d2d16822e23d4d8a836061ba24c4302ebfa565eca | [
"arxiv",
"semantic_scholar"
] | FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System | Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data. However, privacy concerns arise as the aggregated local models on the server may reveal sensitive personal information by inversion attacks. Privacy-preserving methods, such as homomorphic ... | [
"Weizhao Jin",
"Yuhang Yao",
"Shanshan Han",
"Jiajun Gu",
"Carlee Joe-Wong",
"Srivatsan Ravi",
"Salman Avestimehr",
"Chaoyang He"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2023-03-20T00:00:00 | https://arxiv.org/abs/2303.10837 | https://arxiv.org/pdf/2303.10837v3 | 2303.10837 | 10.48550/arXiv.2303.10837 | 131 | 8 | false | null | arXiv.org | 0.5301 |
d75ce1e7ae0f647a3dae5ac640042051d04e197c585b636e1303999c68b0b5d0 | [
"arxiv",
"semantic_scholar"
] | A Privacy Preserving System for Movie Recommendations Using Federated Learning | Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for ... | [
"David Neumann",
"Andreas Lutz",
"Karsten Müller",
"Wojciech Samek"
] | [
"cs.IR",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2023-03-07T00:00:00 | https://arxiv.org/abs/2303.04689 | https://arxiv.org/pdf/2303.04689v4 | 2303.04689 | 10.1145/3634686 | 27 | 0 | false | null | null | 0.3618 |
10b8e852dddbf577edac8c6eb5eea8e6b54f9704aef86c712c9ad1f961edcca7 | [
"arxiv",
"semantic_scholar"
] | Towards Interpretable Federated Learning | Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical ... | [
"Anran Li",
"Rui Liu",
"Ming Hu",
"Yuanyuan Chen",
"Shipeng Wang",
"Lizhen Cui",
"Han Yu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2023-02-27T00:00:00 | https://arxiv.org/abs/2302.13473 | https://arxiv.org/pdf/2302.13473v2 | 2302.13473 | 10.48550/arXiv.2302.13473 | 20 | 1 | false | null | arXiv.org | 0.3306 |
e373e167399a09d57554b0ae93c1e677eedcc97824084f17eb5ec5ac446cf2bb | [
"arxiv",
"semantic_scholar"
] | Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III | Background: Federated learning methods offer the possibility of training machine learning models on privacy-sensitive data sets, which cannot be easily shared. Multiple regulations pose strict requirements on the storage and usage of healthcare data, leading to data being in silos (i.e. locked-in at healthcare faciliti... | [
"Aron N. Horvath",
"Matteo Berchier",
"Farhad Nooralahzadeh",
"Ahmed Allam",
"Michael Krauthammer"
] | [
"cs.LG",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2023-02-08T00:00:00 | https://arxiv.org/abs/2302.04208 | https://arxiv.org/pdf/2302.04208v1 | 2302.04208 | 10.48550/arXiv.2302.04208 | 7 | 0 | true | null | arXiv.org | 0.2258 |
ff9345a062fa324428aad8030b0f26242e509d02b9d8d22b7fda601f8810733b | [
"arxiv",
"semantic_scholar"
] | FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation | Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. In this paper, we ... | [
"Hanlin Gu",
"Jiahuan Luo",
"Yan Kang",
"Lixin Fan",
"Qiang Yang"
] | [
"cs.DC",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2023-01-30T00:00:00 | https://arxiv.org/abs/2301.12623 | https://arxiv.org/pdf/2301.12623v2 | 2301.12623 | 10.48550/arXiv.2301.12623 | 19 | 1 | false | null | International Joint Conference on Artificial Intelligence | 0.3253 |
855ba9a36157d1daf8da169c149e79c1ee2bbfdd29f2ef2b17bb8ce304a1b6dd | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning | Proactive edge association is capable of improving wireless connectivity at the cost of increased handover (HO) frequency and energy consumption, while relying on a large amount of private information sharing required for decision making. In order to improve the connectivity-cost trade-off without privacy leakage, we i... | [
"Yan Lin",
"Jinming Bao",
"Yijin Zhang",
"Jun Li",
"Feng Shu",
"Lajos Hanzo"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2023-01-26T00:00:00 | https://arxiv.org/abs/2301.11014 | https://arxiv.org/pdf/2301.11014v1 | 2301.11014 | 10.1109/TVT.2023.3240682 | 13 | 0 | false | null | IEEE Transactions on Vehicular Technology | 0.2865 |
f14b0e0808f370299f2752b8b8e346e30260b93dfa3634a6b44874f4a57d5330 | [
"arxiv",
"semantic_scholar"
] | Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer | In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of the vision transformer (ViT). The proposed method allows us not only to train... | [
"Teru Nagamori",
"Hitoshi Kiya"
] | [
"cs.CV",
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2023-01-23T00:00:00 | https://arxiv.org/abs/2301.09255 | https://arxiv.org/pdf/2301.09255v2 | 2301.09255 | 10.48550/arXiv.2301.09255 | 4 | 0 | false | null | arXiv.org | 0.1747 |
b20b6b2439fcfe95919a22564fdc5566aba428f3f855c4ea3747edd6bfde4f6d | [
"arxiv",
"semantic_scholar"
] | Privacy and Efficiency of Communications in Federated Split Learning | Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make valuable predictions. Distributed machine learning techniques such as Federated and... | [
"Zongshun Zhang",
"Andrea Pinto",
"Valeria Turina",
"Flavio Esposito",
"Ibrahim Matta"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2023-01-04T00:00:00 | https://arxiv.org/abs/2301.01824 | https://arxiv.org/pdf/2301.01824v2 | 2301.01824 | 10.1109/TBDATA.2023.3280405 | 61 | 2 | false | null | IEEE Transactions on Big Data | 0.4481 |
f17a62bed4d58afa746532c92c77c95193591d163c7d636c9885d802f67bba3f | [
"arxiv",
"semantic_scholar"
] | Privacy Considerations for Risk-Based Authentication Systems | Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be... | [
"Stephan Wiefling",
"Jan Tolsdorf",
"Luigi Lo Iacono"
] | [
"cs.CR",
"cs.HC"
] | [
"Computer Science"
] | 2023-01-04T00:00:00 | https://arxiv.org/abs/2301.01505 | https://arxiv.org/pdf/2301.01505v1 | 2301.01505 | 10.1109/EuroSPW54576.2021.00040 | 13 | 1 | false | null | 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 320-327 | 0.2865 |
f6166aef1114fc9df3f6896747571591d8e2e8d03af9d06b2112eb9431ca97a6 | [
"arxiv",
"semantic_scholar"
] | Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management | The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely us... | [
"Yuanzheng Li",
"Shangyang He",
"Yang Li",
"Yang Shi",
"Zhigang Zeng"
] | [
"eess.SY",
"cs.LG"
] | [
"Computer Science",
"Medicine",
"Engineering"
] | 2022-12-29T00:00:00 | https://arxiv.org/abs/2301.00641 | https://arxiv.org/pdf/2301.00641v1 | 2301.00641 | 10.1109/TNNLS.2022.3232630 | 108 | 3 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.5094 |
c6e3fd69cd6b91a50d99401ab8499cadb62ea03b8da249443d224796ebcc7895 | [
"arxiv",
"semantic_scholar"
] | Social-Aware Clustered Federated Learning with Customized Privacy Preservation | A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy con... | [
"Yuntao Wang",
"Zhou Su",
"Yanghe Pan",
"Tom H Luan",
"Ruidong Li",
"Shui Yu"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2022-12-25T00:00:00 | https://arxiv.org/abs/2212.13992 | https://arxiv.org/pdf/2212.13992v3 | 2212.13992 | 10.1109/TNET.2024.3379439 | 26 | 1 | false | null | IEEE/ACM Transactions on Networking | 0.3578 |
ebef3ba855cd9bb6173437b65d040741fa82782fa2481b5f1d828c74cf3fc452 | [
"arxiv",
"semantic_scholar"
] | Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification | Creating high-performance generalizable deep neural networks for phytoplankton monitoring requires utilizing large-scale data coming from diverse global water sources. A major challenge to training such networks lies in data privacy, where data collected at different facilities are often restricted from being transferr... | [
"Daniel Zhang",
"Vikram Voleti",
"Alexander Wong",
"Jason Deglint"
] | [
"cs.LG",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2022-12-18T00:00:00 | https://arxiv.org/abs/2212.08990 | https://arxiv.org/pdf/2212.08990v1 | 2212.08990 | 10.48550/arXiv.2212.08990 | 1 | 0 | false | null | arXiv.org | 0.0753 |
7e3a5431542343ee509df15f59f841f2fe6f01209d5424e4de3833390d5f58db | [
"arxiv",
"semantic_scholar"
] | Client Selection for Federated Bayesian Learning | Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed learning framework for federated Bayesian learning, where multiple clients jointly train a machine learning model by communicating a number of non-random and interacting particles with the server. Since communication resources are li... | [
"Jiarong Yang",
"Yuan Liu",
"Rahif Kassab"
] | [
"cs.LG",
"eess.SP"
] | [
"Computer Science",
"Engineering"
] | 2022-12-11T00:00:00 | https://arxiv.org/abs/2212.05492 | https://arxiv.org/pdf/2212.05492v2 | 2212.05492 | 10.1109/JSAC.2023.3242720 | 18 | 1 | false | null | IEEE Journal on Selected Areas in Communications | 0.3197 |
29044b1bbbfec891dab59f818adaa391b27e84b694aa0f3a3e68e116d8467e8c | [
"arxiv",
"semantic_scholar"
] | Towards Fleet-wide Sharing of Wind Turbine Condition Information through Privacy-preserving Federated Learning | Terabytes of data are collected by wind turbine manufacturers from their fleets every day. And yet, a lack of data access and sharing impedes exploiting the full potential of the data. We present a distributed machine learning approach that preserves the data privacy by leaving the data on the wind turbines while still... | [
"Lorin Jenkel",
"Stefan Jonas",
"Angela Meyer"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2022-12-07T00:00:00 | https://arxiv.org/abs/2212.03529 | https://arxiv.org/pdf/2212.03529v3 | 2212.03529 | 10.3390/en16176377 | 15 | 1 | false | null | Energies | 0.301 |
f02789cddce5f8f582c343143d8f6163221d1e788f3d65eae7759a8c37809835 | [
"arxiv",
"semantic_scholar"
] | IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning | Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an i... | [
"Vrizlynn L. L. Thing"
] | [
"cs.CR",
"cs.AI",
"cs.CV",
"cs.LG",
"cs.MM"
] | [
"Computer Science"
] | 2022-11-18T00:00:00 | https://arxiv.org/abs/2211.11565 | https://arxiv.org/pdf/2211.11565v1 | 2211.11565 | 10.1109/BigData55660.2022.10020250 | 2 | 0 | false | null | IEEE International Conference on Big Data, IEEE BigData, 2022 | 0.1193 |
14204ec6f1dad1c23b63a8b153e9b543d4ded20e8d2f88cac685cae3f57ba311 | [
"arxiv",
"semantic_scholar"
] | Towards Privacy-Aware Causal Structure Learning in Federated Setting | Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to centralize a large amount of data from multiple data sources. However, in the privacy-preserving setting, it i... | [
"Jianli Huang",
"Xianjie Guo",
"Kui Yu",
"Fuyuan Cao",
"Jiye Liang"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2022-11-13T00:00:00 | https://arxiv.org/abs/2211.06919 | https://arxiv.org/pdf/2211.06919v2 | 2211.06919 | 10.1109/TBDATA.2023.3285477 | 20 | 2 | false | null | IEEE Transactions on Big Data | 0.3306 |
9f8bfcebb9824d145298bc1e110237cd8398fa8aad4599da6aa54f1ddb6a5227 | [
"arxiv",
"semantic_scholar"
] | FL Games: A Federated Learning Framework for Distribution Shifts | Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each hold data from a different distribution, which can yield to catastrophic generalization on data from a different client, which represents a n... | [
"Sharut Gupta",
"Kartik Ahuja",
"Mohammad Havaei",
"Niladri Chatterjee",
"Yoshua Bengio"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-10-31T00:00:00 | https://arxiv.org/abs/2211.00184 | https://arxiv.org/pdf/2211.00184v1 | 2211.00184 | 10.48550/arXiv.2205.11101 | 24 | 2 | false | null | arXiv.org | 0.3495 |
8f6b436b6f6dda4116650051df0720f31a80f104e74b94f34123d2399b82eeea | [
"arxiv",
"semantic_scholar"
] | NVIDIA FLARE: Federated Learning from Simulation to Real-World | Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without centralizing the data. We created NVIDIA FLARE as an open-source software development kit (SDK) to make it easier for data scientists to use FL in their research and real-world ... | [
"Holger R. Roth",
"Yan Cheng",
"Yuhong Wen",
"Isaac Yang",
"Ziyue Xu",
"Yuan-Ting Hsieh",
"Kristopher Kersten",
"Ahmed Harouni",
"Can Zhao",
"Kevin Lu",
"Zhihong Zhang",
"Wenqi Li",
"Andriy Myronenko",
"Dong Yang",
"Sean Yang",
"Nicola Rieke",
"Abood Quraini",
"Chester Chen",
"Da... | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NI",
"cs.SE"
] | [
"Computer Science"
] | 2022-10-24T00:00:00 | https://arxiv.org/abs/2210.13291 | https://arxiv.org/pdf/2210.13291v3 | 2210.13291 | 10.48550/arXiv.2210.13291 | 174 | 7 | true | https://github.com/NVIDIA/NVFlare | IEEE Data Engineering Bulletin | 0.5608 |
1da12d739370d00d3740a9700b570028252684fa618c8db8244f3c1f6359b888 | [
"arxiv",
"semantic_scholar"
] | Federated Learning with Privacy-Preserving Ensemble Attention Distillation | Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to th... | [
"Xuan Gong",
"Liangchen Song",
"Rishi Vedula",
"Abhishek Sharma",
"Meng Zheng",
"Benjamin Planche",
"Arun Innanje",
"Terrence Chen",
"Junsong Yuan",
"David Doermann",
"Ziyan Wu"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science",
"Medicine"
] | 2022-10-16T00:00:00 | https://arxiv.org/abs/2210.08464 | https://arxiv.org/pdf/2210.08464v1 | 2210.08464 | 10.1109/TMI.2022.3213244 | 45 | 2 | false | null | IEEE Transactions on Medical Imaging | 0.4157 |
d360bbc7cf72419c808ad45b34a162ce22e103fca94aad7ad55aebcc89838edd | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph | Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an e... | [
"Yang Lu",
"Zhengxin Yu",
"Neeraj Suri"
] | [
"cs.CR",
"cs.LG",
"cs.MA"
] | [
"Computer Science"
] | 2022-10-01T00:00:00 | https://arxiv.org/abs/2210.00325 | https://arxiv.org/pdf/2210.00325v1 | 2210.00325 | 10.1145/3591354 | 31 | 2 | false | null | ACM Transactions on Privacy and Security | 0.3763 |
bc087fe55f82a42c1261b6d3b878da63eaf2f84392a46d3cb31fd1e0cdcb9720 | [
"arxiv",
"semantic_scholar"
] | Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning | Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization for these domains has already been shown, the impact of other ... | [
"Reza Nasirigerdeh",
"Javad Torkzadehmahani",
"Daniel Rueckert",
"Georgios Kaissis"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2022-09-30T00:00:00 | https://arxiv.org/abs/2210.00053 | https://arxiv.org/pdf/2210.00053v2 | 2210.00053 | 10.1109/SaTML54575.2023.00016 | 1 | 0 | false | null | 1st IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2023 | 0.0753 |
2e8daf396e5da7b3e0a5a9e78c75356b73c1f37e10eefc33520ab8c8a84ee3e9 | [
"arxiv",
"semantic_scholar"
] | Momentum Gradient Descent Federated Learning with Local Differential Privacy | Nowadays, the development of information technology is growing rapidly. In the big data era, the privacy of personal information has been more pronounced. The major challenge is to find a way to guarantee that sensitive personal information is not disclosed while data is published and analyzed. Centralized differential... | [
"Mengde Han",
"Tianqing Zhu",
"Wanlei Zhou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-09-28T00:00:00 | https://arxiv.org/abs/2209.14086 | https://arxiv.org/pdf/2209.14086v2 | 2209.14086 | 10.48550/arXiv.2209.14086 | 0 | 0 | false | null | arXiv.org | 0 |
5be5ac3fda88c8f090ee96c24c99071333ff1bbdafa095e2f67a8f513bad4b17 | [
"arxiv",
"semantic_scholar"
] | FedToken: Tokenized Incentives for Data Contribution in Federated Learning | Incentives that compensate for the involved costs in the decentralized training of a Federated Learning (FL) model act as a key stimulus for clients' long-term participation. However, it is challenging to convince clients for quality participation in FL due to the absence of: (i) full information on the client's data q... | [
"Shashi Raj Pandey",
"Lam Duc Nguyen",
"Petar Popovski"
] | [
"cs.LG",
"cs.DC",
"cs.GT",
"cs.NI"
] | [
"Computer Science"
] | 2022-09-20T00:00:00 | https://arxiv.org/abs/2209.09775 | https://arxiv.org/pdf/2209.09775v2 | 2209.09775 | 10.48550/arXiv.2209.09775 | 17 | 1 | false | null | arXiv.org | 0.3138 |
cef242fb173e62cd6dc9e96ca712065cc7086f6f00b411d9dea0d7394b9e3735 | [
"arxiv",
"semantic_scholar"
] | Concealing Sensitive Samples against Gradient Leakage in Federated Learning | Federated Learning (FL) is a distributed learning paradigm that enhances users privacy by eliminating the need for clients to share raw, private data with the server. Despite the success, recent studies expose the vulnerability of FL to model inversion attacks, where adversaries reconstruct users private data via eaves... | [
"Jing Wu",
"Munawar Hayat",
"Mingyi Zhou",
"Mehrtash Harandi"
] | [
"cs.LG",
"cs.CR",
"cs.CV"
] | [
"Computer Science"
] | 2022-09-13T00:00:00 | https://arxiv.org/abs/2209.05724 | https://arxiv.org/pdf/2209.05724v2 | 2209.05724 | 10.1609/aaai.v38i19.30171 | 18 | 1 | false | null | AAAI Conference on Artificial Intelligence | 0.3197 |
bbc66501a1d1acb815468483417cd88c2c5a8274a404540168a98a38123838f0 | [
"arxiv",
"semantic_scholar"
] | Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning | Federated learning has attracted growing interest as it preserves the clients' privacy. As a variant of federated learning, federated transfer learning utilizes the knowledge from similar tasks and thus has also been intensively studied. However, due to the limited radio spectrum, the communication efficiency of federa... | [
"Feng Wang",
"M. Cenk Gursoy",
"Senem Velipasalar"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-09-12T00:00:00 | https://arxiv.org/abs/2209.05395 | https://arxiv.org/pdf/2209.05395v1 | 2209.05395 | 10.1109/GLOBECOM48099.2022.10000612 | 5 | 0 | true | https://github.com/wfwf10/Feature-based-Federated-Transfer-Learning | Global Communications Conference | 0.1945 |
770a84b733e87ad042bbd9250ab4feac9b4c7bf864925ff467378df62d71617c | [
"arxiv",
"semantic_scholar"
] | Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling | Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally, some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of differ... | [
"Sourav Kumar",
"A. Lakshminarayanan",
"Ken Chang",
"Feri Guretno",
"Ivan Ho Mien",
"Jayashree Kalpathy-Cramer",
"Pavitra Krishnaswamy",
"Praveer Singh"
] | [
"cs.LG",
"cs.AI"
] | [
"Medicine",
"Computer Science"
] | 2022-09-12T00:00:00 | https://arxiv.org/abs/2209.05424 | https://arxiv.org/pdf/2209.05424v1 | 2209.05424 | 10.48550/arXiv.2209.05424 | 10 | 2 | false | null | null | 0.2603 |
95bee04a3de69583e194648db2b41824f2fc35fec3f96d6b7d3efb282d1454a3 | [
"arxiv",
"semantic_scholar"
] | Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation | Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they suffer from com... | [
"Xuan Gong",
"Abhishek Sharma",
"Srikrishna Karanam",
"Ziyan Wu",
"Terrence Chen",
"David Doermann",
"Arun Innanje"
] | [
"cs.CR",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-09-10T00:00:00 | https://arxiv.org/abs/2209.04599 | https://arxiv.org/pdf/2209.04599v1 | 2209.04599 | 10.1609/aaai.v36i11.21446 | 116 | 12 | false | null | AAAI Conference on Artificial Intelligence | 0.557 |
7bf790373d09d155130e9c2826f96208adabcfb644b1080af94e0e009ac19ee9 | [
"arxiv",
"semantic_scholar"
] | Trading Off Privacy, Utility and Efficiency in Federated Learning | Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving \textit{privacy} and maintaining high model \textit{util... | [
"Xiaojin Zhang",
"Yan Kang",
"Kai Chen",
"Lixin Fan",
"Qiang Yang"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2022-09-01T00:00:00 | https://arxiv.org/abs/2209.00230 | https://arxiv.org/pdf/2209.00230v3 | 2209.00230 | 10.1145/3595185 | 80 | 0 | false | null | ACM Transactions on Intelligent Systems and Technology | 0.4771 |
7e6f7debe78d6b09fdd4475544629178ea7469ec169ab4b0a17bf01dbfaf1d01 | [
"arxiv",
"semantic_scholar"
] | FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs | As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some practical scenarios, graph data are stored separately in multiple distributed parties, which may not be directly shared due to conflicts of inte... | [
"Taolin Zhang",
"Chuan Chen",
"Yaomin Chang",
"Lin Shu",
"Zibin Zheng"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2022-08-29T00:00:00 | https://arxiv.org/abs/2208.13685 | https://arxiv.org/pdf/2208.13685v2 | 2208.13685 | 10.1145/3624017 | 34 | 2 | false | null | ACM Transactions on Knowledge Discovery from Data | 0.386 |
afa8bc1a63c4b65c14876234994dc9f04407557374f8724c33198dfd2c99589c | [
"arxiv",
"semantic_scholar"
] | Towards Sparsified Federated Neuroimaging Models via Weight Pruning | Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated t... | [
"Dimitris Stripelis",
"Umang Gupta",
"Nikhil Dhinagar",
"Greg Ver Steeg",
"Paul Thompson",
"José Luis Ambite"
] | [
"cs.LG",
"cs.CR",
"eess.IV",
"q-bio.QM"
] | [
"Computer Science",
"Engineering",
"Biology"
] | 2022-08-24T00:00:00 | https://arxiv.org/abs/2208.11669 | https://arxiv.org/pdf/2208.11669v1 | 2208.11669 | 10.48550/arXiv.2208.11669 | 4 | 0 | false | null | null | 0.1747 |
f32bd8187db69f801a8ef97e0c7891bb741ade1285ce774cd54b5db53e6c2faf | [
"arxiv",
"semantic_scholar"
] | Joint Privacy Enhancement and Quantization in Federated Learning | Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to challenges that are not encountered in centralized machine learning, including the need to preserve the privacy of the local datasets,... | [
"Natalie Lang",
"Elad Sofer",
"Tomer Shaked",
"Nir Shlezinger"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2022-08-23T00:00:00 | https://arxiv.org/abs/2208.10888 | https://arxiv.org/pdf/2208.10888v1 | 2208.10888 | 10.1109/TSP.2023.3244092 | 78 | 9 | false | null | IEEE Transactions on Signal Processing | 0.5 |
572cdae32aa9624aca9e8247eaa9521a56610d7164da68c530bfb160a86ba80e | [
"arxiv",
"semantic_scholar"
] | FedOS: using open-set learning to stabilize training in federated learning | Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, ... | [
"Mohamad Mohamad",
"Julian Neubert",
"Juan Segundo Argayo"
] | [
"stat.ML",
"cs.LG"
] | [
"Mathematics",
"Computer Science"
] | 2022-08-22T00:00:00 | https://arxiv.org/abs/2208.11512 | https://arxiv.org/pdf/2208.11512v2 | 2208.11512 | 10.48550/arXiv.2208.11512 | 2 | 0 | false | null | arXiv.org | 0.1193 |
8128bd8c5764c8108d844ac1e85833c3f6fdd2d9447bdbb635a535efce9910a1 | [
"arxiv",
"semantic_scholar"
] | Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images | Federated learning (FL) is a decentralized method enabling hospitals to collaboratively learn a model without sharing private patient data for training. In FL, participant hospitals periodically exchange training results rather than training samples with a central server. However, having access to model parameters or g... | [
"S. Maryam Hosseini",
"Milad Sikaroudi",
"Morteza Babaei",
"H. R. Tizhoosh"
] | [
"cs.CR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-08-21T00:00:00 | https://arxiv.org/abs/2208.10919 | https://arxiv.org/pdf/2208.10919v1 | 2208.10919 | 10.48550/arXiv.2208.10919 | 25 | 0 | false | null | null | 0.3537 |
a67d7164012c1eeaf7f382e6392b75b94995343bb4c13e67ddc9d67c4af9071c | [
"arxiv",
"semantic_scholar"
] | Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy | Secure multi-party computation-based machine learning, referred to as MPL, has become an important technology to utilize data from multiple parties with privacy preservation. While MPL provides rigorous security guarantees for the computation process, the models trained by MPL are still vulnerable to attacks that solel... | [
"Wenqiang Ruan",
"Mingxin Xu",
"Wenjing Fang",
"Li Wang",
"Lei Wang",
"Weili Han"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2022-08-18T00:00:00 | https://arxiv.org/abs/2208.08662 | https://arxiv.org/pdf/2208.08662v1 | 2208.08662 | 10.1109/SP46215.2023.10179422 | 25 | 5 | true | null | IEEE Symposium on Security and Privacy | 0.3891 |
44bade6be91f71b0a0c404de6c3c5e02359c4bec47595199d9ccf45c482319ea | [
"arxiv",
"semantic_scholar"
] | Federated Quantum Natural Gradient Descent for Quantum Federated Learning | The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm for the QFL is expected to minimize the communication overhead among different quantum participants. In this work, we put forth an efficient l... | [
"Jun Qi"
] | [
"quant-ph",
"cs.DC",
"cs.LG"
] | [
"Computer Science",
"Physics"
] | 2022-08-15T00:00:00 | https://arxiv.org/abs/2209.00564 | https://arxiv.org/pdf/2209.00564v1 | 2209.00564 | 10.48550/arXiv.2209.00564 | 27 | 1 | false | null | arXiv.org | 0.3618 |
743a7cfdeeb19d2bc1de3d19735efefba466aee0ea9636e2dd341a4fad363907 | [
"arxiv",
"semantic_scholar"
] | How Much Privacy Does Federated Learning with Secure Aggregation Guarantee? | Federated learning (FL) has attracted growing interest for enabling privacy-preserving machine learning on data stored at multiple users while avoiding moving the data off-device. However, while data never leaves users' devices, privacy still cannot be guaranteed since significant computations on users' training data a... | [
"Ahmed Roushdy Elkordy",
"Jiang Zhang",
"Yahya H. Ezzeldin",
"Konstantinos Psounis",
"Salman Avestimehr"
] | [
"cs.LG",
"cs.CR",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2022-08-03T00:00:00 | https://arxiv.org/abs/2208.02304 | https://arxiv.org/pdf/2208.02304v1 | 2208.02304 | 10.48550/arXiv.2208.02304 | 49 | 3 | false | null | Proceedings on Privacy Enhancing Technologies | 0.4247 |
129399054d87c73232b6763229fdd8f7b9fc4e0cb7e77ab5402b452f056fef30 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Federated Recurrent Neural Networks | We present RHODE, a novel system that enables privacy-preserving training of and prediction on Recurrent Neural Networks (RNNs) in a cross-silo federated learning setting by relying on multiparty homomorphic encryption. RHODE preserves the confidentiality of the training data, the model, and the prediction data; and it... | [
"Sinem Sav",
"Abdulrahman Diaa",
"Apostolos Pyrgelis",
"Jean-Philippe Bossuat",
"Jean-Pierre Hubaux"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2022-07-28T00:00:00 | https://arxiv.org/abs/2207.13947 | https://arxiv.org/pdf/2207.13947v2 | 2207.13947 | 10.48550/arXiv.2207.13947 | 11 | 0 | false | null | Proceedings on Privacy Enhancing Technologies | 0.2698 |
c715eb9cc32affe45cd59e6571a50d538f505f17c8d427b66f9cdb87f1c12e55 | [
"arxiv",
"semantic_scholar"
] | Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM | Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL), which allows each client to collect partial features, has attracted intensive research efforts recently. We identified the main challenge... | [
"Chulin Xie",
"Pin-Yu Chen",
"Qinbin Li",
"Arash Nourian",
"Ce Zhang",
"Bo Li"
] | [
"cs.LG",
"cs.CR"
] | [
"Computer Science"
] | 2022-07-20T00:00:00 | https://arxiv.org/abs/2207.10226 | https://arxiv.org/pdf/2207.10226v4 | 2207.10226 | 10.1109/SaTML59370.2024.00029 | 23 | 1 | false | null | null | 0.3451 |
4af06b6e1a3791f174f6c20de3e2bc825fcc8f35ab2e8c138bebd0041fbc5da0 | [
"arxiv",
"semantic_scholar"
] | Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR | Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that training data stay on devices (such as smartphones), and only learned models are shared with a centralized server. In the case of supervised lear... | [
"Sannara Ek",
"Romain Rombourg",
"François Portet",
"Philippe Lalanda"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-07-17T00:00:00 | https://arxiv.org/abs/2207.08187 | https://arxiv.org/pdf/2207.08187v1 | 2207.08187 | 10.1109/PerComWorkshops53856.2022.9767369 | 10 | 1 | false | null | null | 0.2603 |
a1ecc5222a4e05d98931d59c416473d5810eb056746ada5bb1d0b62dea709202 | [
"arxiv",
"semantic_scholar"
] | Federated Continual Learning through distillation in pervasive computing | Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. Current solutions rely on the availability of large amounts of stored data at the client ... | [
"Anastasiia Usmanova",
"François Portet",
"Philippe Lalanda",
"German Vega"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-07-17T00:00:00 | https://arxiv.org/abs/2207.08181 | https://arxiv.org/pdf/2207.08181v1 | 2207.08181 | 10.1109/smartcomp55677.2022.00027 | 15 | 1 | false | null | International Conference on Smart Computing | 0.301 |
56980bf948ac238833d0f10d28ad9e9f6acf9b7d3447d6117ead199a41f663d9 | [
"arxiv",
"semantic_scholar"
] | Enhanced Security and Privacy via Fragmented Federated Learning | In federated learning (FL), a set of participants share updates computed on their local data with an aggregator server that combines updates into a global model. However, reconciling accuracy with privacy and security is a challenge to FL. On the one hand, good updates sent by honest participants may reveal their priva... | [
"Najeeb Moharram Jebreel",
"Josep Domingo-Ferrer",
"Alberto Blanco-Justicia",
"David Sanchez"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science",
"Medicine"
] | 2022-07-13T00:00:00 | https://arxiv.org/abs/2207.05978 | https://arxiv.org/pdf/2207.05978v2 | 2207.05978 | 10.1109/TNNLS.2022.3212627 | 45 | 5 | false | null | IEEE Transactions on Neural Networks and Learning Systems | 0.4157 |
52d053434ac0ff78ebfb9f85295f086adef7f28ff6504170bd86f9204050641e | [
"arxiv",
"semantic_scholar"
] | Efficient and Privacy Preserving Group Signature for Federated Learning | Federated Learning (FL) is a Machine Learning (ML) technique that aims to reduce the threats to user data privacy. Training is done using the raw data on the users' device, called clients, and only the training results, called gradients, are sent to the server to be aggregated and generate an updated model. However, we... | [
"Sneha Kanchan",
"Jae Won Jang",
"Jun Yong Yoon",
"Bong Jun Choi"
] | [
"cs.CR",
"cs.LG",
"cs.NI"
] | [
"Computer Science"
] | 2022-07-12T00:00:00 | https://arxiv.org/abs/2207.05297 | https://arxiv.org/pdf/2207.05297v2 | 2207.05297 | 10.48550/arXiv.2207.05297 | 19 | 0 | false | null | Future generations computer systems | 0.3253 |
b7e4e6d0738b28edbfe1247ebf256820e1f04dfe5366e247af944842494bb64d | [
"arxiv",
"semantic_scholar"
] | Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms | The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transf... | [
"Ehsan Hallaji",
"Roozbeh Razavi-Far",
"Mehrdad Saif"
] | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV",
"cs.DC"
] | [
"Computer Science"
] | 2022-07-05T00:00:00 | https://arxiv.org/abs/2207.02337 | https://arxiv.org/pdf/2207.02337v1 | 2207.02337 | 10.1007/978-3-031-11748-0_3 | 16 | 0 | false | null | arXiv.org | 0.3076 |
f0946ba12781b05921a58eb580c677242cda188138bc4f7f703b13eca618d4de | [
"arxiv",
"semantic_scholar"
] | Towards Federated Long-Tailed Learning | Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks. Recent attempts have been launched to, on one side, address the problem of learning from pervasive private data, and on the other side, learn from long-tailed data. However, both assumptions might hold in practical a... | [
"Zihan Chen",
"Songshang Liu",
"Hualiang Wang",
"Howard H. Yang",
"Tony Q. S. Quek",
"Zuozhu Liu"
] | [
"cs.LG",
"cs.AI",
"cs.DC"
] | [
"Computer Science"
] | 2022-06-30T00:00:00 | https://arxiv.org/abs/2206.14988 | https://arxiv.org/pdf/2206.14988v1 | 2206.14988 | 10.48550/arXiv.2206.14988 | 15 | 0 | false | null | arXiv.org | 0.301 |
4839305255956c49dac710d77ddfa478032f54b2a8b2a98c5822e01907371bb9 | [
"arxiv",
"semantic_scholar"
] | Privacy-preserving Graph Analytics: Secure Generation and Federated Learning | Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships. In particular, we discuss two directions, namely privacy-preserving graph generation a... | [
"Dongqi Fu",
"Jingrui He",
"Hanghang Tong",
"Ross Maciejewski"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2022-06-30T00:00:00 | https://arxiv.org/abs/2207.00048 | https://arxiv.org/pdf/2207.00048v1 | 2207.00048 | 10.48550/arXiv.2207.00048 | 4 | 0 | false | null | arXiv.org | 0.1747 |
cb01a8212a844954ec8cf450d7e9950b01e3dca2baa22469a94d5d64ebe4d5f2 | [
"arxiv",
"semantic_scholar"
] | Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning | An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very different distributions. \emph{System heterogeneity} refers to client devices having different system capabilities. A considerable number of federa... | [
"John Nguyen",
"Jianyu Wang",
"Kshitiz Malik",
"Maziar Sanjabi",
"Michael Rabbat"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-06-30T00:00:00 | https://arxiv.org/abs/2206.15387 | https://arxiv.org/pdf/2206.15387v3 | 2206.15387 | null | 20 | 0 | true | https://github.com/facebookresearch/where_to_begin}} | International Conference on Learning Representations 2023 | 0.3306 |
36016b4b4e12e09cac04d2793f6606a3bc0ca66091702437d40f8dacd1c0965e | [
"arxiv",
"semantic_scholar"
] | FLVoogd: Robust And Privacy Preserving Federated Learning | In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign maj... | [
"Yuhang Tian",
"Rui Wang",
"Yanqi Qiao",
"Emmanouil Panaousis",
"Kaitai Liang"
] | [
"cs.CR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-06-24T00:00:00 | https://arxiv.org/abs/2207.00428 | https://arxiv.org/pdf/2207.00428v1 | 2207.00428 | 10.48550/arXiv.2207.00428 | 5 | 0 | false | null | Asian Conference on Machine Learning | 0.1945 |
1c2a1fc0bd291ab5cc390b6048b9bd91ade5336179b40cd563c2cbed316b444a | [
"arxiv",
"semantic_scholar"
] | FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis | In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support ... | [
"Matteo Pennisi",
"Federica Proietto Salanitri",
"Giovanni Bellitto",
"Bruno Casella",
"Marco Aldinucci",
"Simone Palazzo",
"Concetto Spampinato"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2022-06-20T00:00:00 | https://arxiv.org/abs/2206.10048 | https://arxiv.org/pdf/2206.10048v3 | 2206.10048 | 10.1016/j.cviu.2023.103882 | 20 | 1 | true | https://github.com/perceivelab/FedER | Computer Vision and Image Understanding | 0.3306 |
b3000dddd61abe7db51b997d9f8661f1c1aa2a30795561051c6ffc20a1d7626b | [
"arxiv",
"semantic_scholar"
] | Adaptive Expert Models for Personalization in Federated Learning | Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-Independent and Identically Distributed (non-IID). We propose a practical and robust approach to pers... | [
"Martin Isaksson",
"Edvin Listo Zec",
"Rickard Cöster",
"Daniel Gillblad",
"Šarūnas Girdzijauskas"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-06-15T00:00:00 | https://arxiv.org/abs/2206.07832 | https://arxiv.org/pdf/2206.07832v1 | 2206.07832 | 10.48550/arXiv.2206.07832 | 7 | 0 | false | null | arXiv.org | 0.2258 |
255f06c90bfb80f8f24b9b8856566c3c34274b72aa4554e0f5664e6b0c352b97 | [
"arxiv",
"semantic_scholar"
] | MammoFL: Mammographic Breast Density Estimation using Federated Learning | In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were... | [
"Ramya Muthukrishnan",
"Angelina Heyler",
"Keshava Katti",
"Sarthak Pati",
"Walter Mankowski",
"Aprupa Alahari",
"Michael Sanborn",
"Emily F. Conant",
"Christopher Scott",
"Stacey Winham",
"Celine Vachon",
"Pratik Chaudhari",
"Despina Kontos",
"Spyridon Bakas"
] | [
"eess.IV",
"cs.CV",
"cs.DC",
"cs.LG"
] | [
"Engineering",
"Computer Science"
] | 2022-06-11T00:00:00 | https://arxiv.org/abs/2206.05575 | https://arxiv.org/pdf/2206.05575v5 | 2206.05575 | null | 4 | 0 | false | null | null | 0.1747 |
2000ce37acfc77ac6f9928cb1288eaaa7004d7702f9ec4ec38902622fe31d745 | [
"arxiv",
"semantic_scholar"
] | Federated Learning with GAN-based Data Synthesis for Non-IID Clients | Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distributed (non-IID) data among clients. In this paper, we propose a novel framework, named Synthetic Data Aided Federated Learning (SDA-FL), to res... | [
"Zijian Li",
"Jiawei Shao",
"Yuyi Mao",
"Jessie Hui Wang",
"Jun Zhang"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-06-11T00:00:00 | https://arxiv.org/abs/2206.05507 | https://arxiv.org/pdf/2206.05507v1 | 2206.05507 | 10.48550/arXiv.2206.05507 | 64 | 9 | false | null | null | 0.5 |
2ea81fe9903ae2b27d3f20d8d713c48eb69057acbda4438f21278cf3285b4f35 | [
"arxiv",
"semantic_scholar"
] | Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance | Machine learning abilities have become a vital component for various solutions across industries, applications, and sectors. Many organizations seek to leverage AI-based solutions across their business services to unlock better efficiency and increase productivity. Problems, however, can arise if there is a lack of qua... | [
"Riadh Ben Chaabene",
"Darine Amayed",
"Mohamed Cheriet"
] | [
"cs.LG",
"cs.AI",
"cs.CR"
] | [
"Computer Science"
] | 2022-06-09T00:00:00 | https://arxiv.org/abs/2206.04731 | https://arxiv.org/pdf/2206.04731v1 | 2206.04731 | 10.48550/arXiv.2206.04731 | 1 | 0 | false | null | arXiv.org | 0.0753 |
3e8cb4bee575001ff4b6ef42b998980530d2a328c40499c0b0111f70bc6bde6c | [
"arxiv",
"semantic_scholar"
] | Certified Robustness in Federated Learning | Federated learning has recently gained significant attention and popularity due to its effectiveness in training machine learning models on distributed data privately. However, as in the single-node supervised learning setup, models trained in federated learning suffer from vulnerability to imperceptible input transfor... | [
"Motasem Alfarra",
"Juan C. Pérez",
"Egor Shulgin",
"Peter Richtárik",
"Bernard Ghanem"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-06-06T00:00:00 | https://arxiv.org/abs/2206.02535 | https://arxiv.org/pdf/2206.02535v2 | 2206.02535 | 10.48550/arXiv.2206.02535 | 10 | 2 | false | null | arXiv.org | 0.2603 |
f7a919f815d5df9115d63d5c28cdeeb210b98d833f12568bd260b7be31e597d6 | [
"arxiv",
"semantic_scholar"
] | Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data | Federated learning (FL) is a privacy-promoting framework that enables potentially large number of clients to collaboratively train machine learning models. In a FL system, a server coordinates the collaboration by collecting and aggregating clients' model updates while the clients' data remains local and private. A maj... | [
"Huancheng Chen",
"Haris Vikalo"
] | [
"cs.LG",
"cs.CR",
"cs.DC"
] | [
"Computer Science"
] | 2022-06-01T00:00:00 | https://arxiv.org/abs/2206.00686 | https://arxiv.org/pdf/2206.00686v2 | 2206.00686 | 10.1109/CVPRW59228.2023.00531 | 23 | 2 | false | null | null | 0.3451 |
79c510ef6a14af45e9d3025025ca52e41c64af7a6fe88570482c25aed536b928 | [
"arxiv",
"semantic_scholar"
] | LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation | In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new influence approximation called "lazy influence" to filter and sc... | [
"Ljubomir Rokvic",
"Panayiotis Danassis",
"Sai Praneeth Karimireddy",
"Boi Faltings"
] | [
"cs.CR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2022-05-23T00:00:00 | https://arxiv.org/abs/2205.11518 | https://arxiv.org/pdf/2205.11518v4 | 2205.11518 | 10.1109/BigData62323.2024.10825097 | 3 | 0 | false | null | BigData Congress [Services Society] | 0.1505 |
2f4ac312053d98f701954b5314fc29efccb32f1318d9d5d334d90784439ec3b0 | [
"arxiv",
"semantic_scholar"
] | FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders | Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the privacy issues of public face datasets have attracted increasing attention in recent years. Previous works simply mask most areas of faces ... | [
"Kai Wang",
"Bo Zhao",
"Xiangyu Peng",
"Zheng Zhu",
"Jiankang Deng",
"Xinchao Wang",
"Hakan Bilen",
"Yang You"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2022-05-23T00:00:00 | https://arxiv.org/abs/2205.11090 | https://arxiv.org/pdf/2205.11090v1 | 2205.11090 | 10.48550/arXiv.2205.11090 | 12 | 1 | false | null | arXiv.org | 0.2785 |
85c531ddee786e57de0f8e67051354ef27f1d5721cad6230f39cc0d1abab7aff | [
"arxiv",
"semantic_scholar"
] | Transfer Learning with Pre-trained Conditional Generative Models | Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding... | [
"Shin'ya Yamaguchi",
"Sekitoshi Kanai",
"Atsutoshi Kumagai",
"Daiki Chijiwa",
"Hisashi Kashima"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2022-04-27T00:00:00 | https://arxiv.org/abs/2204.12833 | https://arxiv.org/pdf/2204.12833v3 | 2204.12833 | 10.1007/s10994-025-06748-7 | 7 | 1 | false | null | Machine-mediated learning | 0.2258 |
bf298b5a18eefc774475c0674a46745716a516a633d2b816bc9962a4735faa8c | [
"arxiv",
"semantic_scholar"
] | Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System | Decentralized learning is an efficient emerging paradigm for boosting the computing capability of multiple bounded computing agents. In the big data era, performing inference within the distributed and federated learning (DL and FL) frameworks, the central server needs to process a large amount of data while relying on... | [
"Mohamed Ridha Znaidi",
"Gaurav Gupta",
"Paul Bogdan"
] | [
"cs.MA",
"cs.CR",
"cs.IT",
"cs.LG"
] | [
"Computer Science",
"Mathematics"
] | 2022-04-24T00:00:00 | https://arxiv.org/abs/2205.04855 | https://arxiv.org/pdf/2205.04855v1 | 2205.04855 | 10.48550/arXiv.2205.04855 | 2 | 0 | false | null | International Symposium on Information Theory | 0.1193 |
fad7bee52d3e58b8c9caf34c695f9c15d4ee6d571b84b929dc991263bb051bb1 | [
"arxiv",
"semantic_scholar"
] | FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation | Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus on centralized data, where abundant and high-quality negative samples are easy to obtain. However, centralized user data storage and exploita... | [
"Chuhan Wu",
"Fangzhao Wu",
"Tao Qi",
"Yongfeng Huang",
"Xing Xie"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2022-04-21T00:00:00 | https://arxiv.org/abs/2204.09850 | https://arxiv.org/pdf/2204.09850v1 | 2204.09850 | 10.48550/arXiv.2204.09850 | 24 | 1 | false | null | arXiv.org | 0.3495 |
9a241d072f1ffe8f71ba8b5af21bbedc701c4903abbb4d70de6426aeb7b17c50 | [
"arxiv",
"semantic_scholar"
] | Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case | Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machine... | [
"Febrianti Wibawa",
"Ferhat Ozgur Catak",
"Salih Sarp",
"Murat Kuzlu",
"Umit Cali"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-16T00:00:00 | https://arxiv.org/abs/2204.07752 | https://arxiv.org/pdf/2204.07752v1 | 2204.07752 | 10.1145/3528580.3532845 | 108 | 5 | false | null | European Interdisciplinary Cybersecurity Conference | 0.5094 |
d4ed64c5a7ce2322be24fd0435d091a652875295c065b9d5adb4777aa2d758cf | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption | Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server. Although FL preserves local data privacy to some extent, it has been shown that info... | [
"Haleh Hayati",
"Carlos Murguia",
"Nathan van de Wouw"
] | [
"cs.LG",
"cs.CR",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2022-04-05T00:00:00 | https://arxiv.org/abs/2204.02497 | https://arxiv.org/pdf/2204.02497v2 | 2204.02497 | 10.1109/CDC51059.2022.9993113 | 8 | 0 | false | null | IEEE Conference on Decision and Control | 0.2386 |
30cf41c56acf2dd7fb3e1e968ea383292fd08dda38cb35317ba3166199a5fcc3 | [
"arxiv",
"semantic_scholar"
] | Privacy-Preserving Aggregation in Federated Learning: A Survey | Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individuall... | [
"Ziyao Liu",
"Jiale Guo",
"Wenzhuo Yang",
"Jiani Fan",
"Kwok-Yan Lam",
"Jun Zhao"
] | [
"cs.CR"
] | [
"Computer Science"
] | 2022-03-31T00:00:00 | https://arxiv.org/abs/2203.17005 | https://arxiv.org/pdf/2203.17005v2 | 2203.17005 | 10.48550/arXiv.2203.17005 | 159 | 8 | true | null | IEEE Transactions on Big Data | 0.551 |
1043d6ae602d23400cc47dc62295fa127c27ed75e76042ae6bcf0c5b1e08b43a | [
"arxiv",
"semantic_scholar"
] | FedVLN: Privacy-preserving Federated Vision-and-Language Navigation | Data privacy is a central problem for embodied agents that can perceive the environment, communicate with humans, and act in the real world. While helping humans complete tasks, the agent may observe and process sensitive information of users, such as house environments, human activities, etc. In this work, we introduc... | [
"Kaiwen Zhou",
"Xin Eric Wang"
] | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-03-28T00:00:00 | https://arxiv.org/abs/2203.14936 | https://arxiv.org/pdf/2203.14936v3 | 2203.14936 | 10.48550/arXiv.2203.14936 | 13 | 2 | false | null | European Conference on Computer Vision | 0.2865 |
7779e02d918fdd4949b570bb9bc5bd8e582c1b2b8123ae70e45d7ba1c1060fb9 | [
"arxiv",
"semantic_scholar"
] | Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey | Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy... | [
"Mansoor Ali",
"Faisal Naeem",
"Muhammad Tariq",
"Geroges Kaddoum"
] | [
"eess.SY",
"cs.CR",
"cs.LG"
] | [
"Computer Science",
"Engineering",
"Medicine"
] | 2022-03-18T00:00:00 | https://arxiv.org/abs/2203.09702 | https://arxiv.org/pdf/2203.09702v1 | 2203.09702 | 10.1109/JBHI.2022.3181823 | 252 | 11 | false | null | IEEE journal of biomedical and health informatics | 0.6008 |
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