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arXiv AI By Chaoyu Zhang, Shanghao Shi, Heng Jin, Ning Wang, Y. Thomas Hou, Wenjing Lou

Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning

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Aegis is a client‑side defense for medical federated learning that protects against model inversion attacks by adding a masking gradient derived from locally synthesized data. The method exploits the fact that attacks fail when the effective batch size exceeds the model’s leakage capacity, turning this bottleneck into a privacy guarantee. Experiments on MNIST, CIFAR‑10, and MedMNIST datasets show that Aegis neutralizes state‑of‑the‑art attacks while preserving model accuracy and adding only modest overhead.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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