Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
Federated learning (FL) has become a foundational paradigm for multi-institutional medical AI, allowing hospitals and research centers to jointly train diagnostic models without exchanging patient records. This privacy promise, however, is increasingly contested: a malicious or honest-but-curious server can launch model inversion attacks (MIAs) that reconstruct private patient images directly from shared model updates, and recent scalable, closed-form attacks penetrate even secure aggregation at clinically realistic batch sizes. Existing defenses face an unsatisfactory dilemma. Gradient-perturbation methods such as differential privacy and pruning trade away the diagnostic accuracy on whi...
| Authors | Chaoyu Zhang, Shanghao Shi, Heng Jin et al. |
|---|---|
| Primary category | cs.CR |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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