FedRC: Coordinating Knowledge Sharing in Federated Learning Assisted Mobile Computing

While federated learning (FL) is promising for collaborative training that preserves privacy on the mobile Internet, it remains vulnerable to data heterogeneity, model personalization, and user mobility. A viable paradigm for tackling these challenges is knowledge distillation (KD) based FL, which enables knowledge sharing between user-customized models. However, misleading and ambiguous knowledge sharing arises due to discrepancies in personal data distribution, the absence of a public dataset or a well-trained teacher model, which results in significant degradation in convergence and model performance. To address this issue, we propose a novel scheme named “coordinated learning through peer teaching,” which integrates conceptual alignments (CAs) from cognitive science into mainstream FL systems. The proposed method involves a novel rotational CA coordinator mechanism for model aggregation, for which the server takes turns choosing a CA coordinator from participating clients and arranges them to align concepts via asymmetric KD on the party side. By incorporating this mechanism into iterative training, we introduce FedRC, a new federated learning framework that combines recent advances in channel distillation and decoupled knowledge distillation for FL-assisted mobile computing. We evaluated our approach through extensive experiments on three real-world datasets that feature degrees of data and model heterogeneity. Empirical results indicate the efficacy of FedRC in improving transfer efficiency while notably improving the accuracy of the client population without using public data. Compared with state-of-the-art FL methods, FedRC outperforms baselines in standard and realistic mobile FL settings.