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Federated learning (FL) has emerged as a privacy-preserving paradigm thattrains neural networks on edge devices without collecting data at a centralserver. However, FL encounters an inherent challenge in dealing withnon-independent and identically distributed (non-IID) data among devices. Toaddress this challenge, this paper proposes a hard feature matching datasynthesis (HFMDS) method to share auxiliary data besides local models.Specifically, synthetic data are generated by learning the essentialclass-relevant features of real samples and discarding the redundant features,which helps to effectively tackle the non-IID issue. For better privacypreservation, we propose a hard feature augmentation method to transfer realfeatures towards the decision boundary, with which the synthetic data not onlyimprove the model generalization but also erase the information of realfeatures. By integrating the proposed HFMDS method with FL, we present a novelFL framework with data augmentation to relieve data heterogeneity. Thetheoretical analysis highlights the effectiveness of our proposed datasynthesis method in solving the non-IID challenge. Simulation results furtherdemonstrate that our proposed HFMDS-FL algorithm outperforms the baselines interms of accuracy, privacy preservation, and computational cost on variousbenchmark datasets.

Feature Matching Data Synthesis for Non-IID Federated Learning