Heterogeneous Privacy Level-Based Client Selection for Hybrid Federated and Centralized Learning in Mobile Edge Computing

To alleviate the substantial local training burden on clients in the federated learning (FL) process, this paper proposes a more efficient approach based on hybrid federated and centralized learning (HFCL), leveraging the Mobile Edge Computing (MEC) environment within wireless communication networks. Considering the existence of heterogeneous data types with different privacy levels -such as 1) sensitive data, which can not be exposed, and 2) less-sensitive data, which can be exposed for centralized learning (CL)-we formulate an optimization problem aimed at achieving a balance between 1) total latency, including computation and communication, and 2) the training burden on the MEC server. This balance is achieved by adjusting the set of participants in FL, taking into account client selection under different privacy levels. A multi-objective optimization problem is designed using mixed-integer nonlinear programming, which is generally recognized as NP-hard. We employ relaxation techniques in combination with the Mutas & Simulated Annealing Heuristic algorithm to develop a near-optimal yet practical algorithm. Our numerical and simulation results reveal that the proposed scheme effectively achieves a global model by striking a balance between the total time required for model convergence and the computational load on the MEC server. Furthermore, experimental results on three well-known real-world datasets demonstrate that the proposed scheme maintains an acceptable level of accuracy and loss.