FedDAR: Federated Learning With Data-Quantity Aware Regularization for Heterogeneous Distributed Data

Federated learning (FL) has emerged as a promising approach for collaboratively training global models and classifiers without sharing private data. However, existing studies primarily focus on distinct methodologies for typical and personalized FL (tFL and pFL), representing a challenge in explorin...

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