FedHPro learns hyper-prototypes via gradient matching and mutual-contrastive learning to achieve state-of-the-art performance in heterogeneous federated learning.
However, as shown in Table A7, implementing FedProto, FedTGP, and FedSA following their original prototype-only communication setting consistently leads to poor performance
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FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
FedHPro learns hyper-prototypes via gradient matching and mutual-contrastive learning to achieve state-of-the-art performance in heterogeneous federated learning.