Multi-Task Personalized Federated Learning for Tailored Services in Mobile Edge Computing

Personalized Federated Learning (PFL) has been widely adopted in Mobile Edge Computing (MEC) to enable tailored services without private user data ever leaving the devices. Previous efforts have illustrated that the weighted aggregation determined by the number of samples on clients will hurt the convergence of PFL. Furthermore, the over-personalization of PFL may result in model overfitting and lack of generalization capabilities. Therefore, in this paper, we propose novel quality/discrepancy-aware Multi-task Personalized Federated Learning (MPFL) for MEC to tackle these issues. Specifically, a number of personalized learning objectives of different clients in PFL are considered as multiple tasks. Both local private Batch Normalization (BN) and global shared BN layers are introduced into PFL as the specialized experts to better balance the personalization and generalization capabilities of the local models. Furthermore, the statistical discrepancies between such two BN layers and the model quality of clients are jointly taken into account to design aggregation weights for quick model aggregation with better performance gains. Extensive experiments conducted on three well-known datasets demonstrate that the proposed framework outperforms the state-of-the-art benchmarks in terms of convergence speed and learning accuracy.