{
 "kind": "cnn-1conv-2dense",
 "note": "The FHE-feasible medical student, layer by layer. Evaluate strictly in this order: conv (valid convolution over the input zero-padded by `conv.padding`), conv.activation, average pool by `pool.stride`, flatten channel-major, then each dense with its own `activation`. Every activation is recorded from the trained module order rather than assumed - the conv is followed by a ReLU, and omitting it still reproduces every argmax on the test split while shifting the logits, which is why it is stated here explicitly. The browser runs conv and both dense layers under CKKS and the ReLU/argmax under MPC, the hybrid this project describes.",
 "teacher_key": "medical_fl.resnet18",
 "student_key": "medical_fl.student",
 "input": {
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  "preprocessing": [
   "divide by 255",
   "subtract 0.5",
   "divide by 0.5",
   "resize to 14x14"
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  "out_ch": 4,
  "kernel": 3,
  "padding": 1,
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