  Federated learning (FL) has emerged as a privacy-preserving paradigm that
trains neural networks on edge devices without collecting data at a central
server. However, FL encounters an inherent challenge in dealing with
non-independent and identically distributed (non-IID) data among devices. To
address this challenge, this paper proposes a hard feature matching data
synthesis (HFMDS) method to share auxiliary data besides local models.
Specifically, synthetic data are generated by learning the essential
class-relevant features of real samples and discarding the redundant features,
which helps to effectively tackle the non-IID issue. For better privacy
preservation, we propose a hard feature augmentation method to transfer real
features towards the decision boundary, with which the synthetic data not only
improve the model generalization but also erase the information of real
features. By integrating the proposed HFMDS method with FL, we present a novel
FL framework with data augmentation to relieve data heterogeneity. The
theoretical analysis highlights the effectiveness of our proposed data
synthesis method in solving the non-IID challenge. Simulation results further
demonstrate that our proposed HFMDS-FL algorithm outperforms the baselines in
terms of accuracy, privacy preservation, and computational cost on various
benchmark datasets.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing