FedDAR: Federated Learning With Data-Quantity Aware Regularization for Heterogeneous Distributed Data
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DOI: 10.1109/access.2025.3591839
Abstract: 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… read more here.
Keywords: data quantity; quantity aware; aware regularization; federated learning ... See more keywords