Federated Contrastive Diffusion Prototypes for Robust Private Learning

Summary

Researchers introduce Federated Contrastive Diffusion Prototypes (Fed-CDP), a method that has the server turn aggregated client prototypes into synthesized features for robust federated learning. On CIFAR-100 under severe heterogeneity (α=0.1), Fed-CDP surpasses leading methods by nearly 5% in standard accuracy and over 9% in robust accuracy under Projected Gradient Descent attacks.

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