DevSFL: Deviation-Aware Split Federated Learning in Resource-Constrained Wireless Networks

In mobile wireless networks, data heterogeneity and resource constraints cause performance degradation in machine learning tasks on edge clients. To alleviate these issues, we propose a novel deviation-aware split federated learning (DevSFL) framework, which adopts an adaptive aggregation weight determination method for mitigating the effects of data heterogeneity across local datasets and improving overall learning performance. Leveraging Lyapunov optimization, we formulate a comprehensive optimization problem including client scheduling, cut layer selection, bandwidth allocation, and weight decisionmaking to enhance resource utilization and energy efficiency. To tackle this problem, we employ a sample average approximation based algorithm and a dichotomy method for optimizing cut layer selection and bandwidth allocation policies, respectively. Furthermore, a set expansion algorithm is employed to find the optimal client subset. Additionally, we introduce a deviationaware algorithm specifically designed to refine the weighting policy. Comparative analysis with benchmark schemes reveals that our proposed DevSFL framework not only achieves higher accuracy within fewer rounds but also significantly reduces the time required to reach a predefined accuracy level, thereby demonstrating the effectiveness of our proposed algorithms.