FedMate improves personalized federated learning by recalibrating global prototype aggregation weights and training local classifiers with dual adversarial discriminators, outperforming state-of-the-art baselines on six datasets.
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Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination
FedMate improves personalized federated learning by recalibrating global prototype aggregation weights and training local classifiers with dual adversarial discriminators, outperforming state-of-the-art baselines on six datasets.