Author

Harsh Kumar

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#federated learning Jul 2026

FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging

This work proposes a prototype-based, influence-aware federated learning framework (FedProIn) that uses multiple learnable class prototypes to capture shared semantic structures across heterogeneous clients and introduces feature divergence loss and prototype contrastive loss to mitigate client drift by decomposing it...

Harsh Kumar, T. Garg, V. Sundaresan · Lecture notes in computer sc... · 0 citations