FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging
Federated learning (FL) is severely hindered by statistical heterogeneity due to variations in scanners, acquisition protocols, and patient populations. Such non-IID data induces client drift during local optimization, leading to unstable convergence and suboptimal global models when parameter-based aggregation is applied. We propose a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients. We introduce feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it into feature drift and prototype drift. In addition, we propo...
| Authors | Harsh Kumar, Tarun Kumar Garg, Vaanathi Sundaresan |
|---|---|
| Primary category | eess.IV |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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