COSMOS performs model-agnostic personalized federated learning via server-side clustering on pseudo-label predictions and distillation of cluster models, claiming exponential personalization risk contraction.
During client fine-tuning (using their local data true labels), we use Cross-Entropy Loss (nn.CrossEntropyLoss)
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COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
COSMOS performs model-agnostic personalized federated learning via server-side clustering on pseudo-label predictions and distillation of cluster models, claiming exponential personalization risk contraction.