Tackling Class Imbalance and Client Heterogeneity for Split Federated Learning in Wireless Networks

As the complexity of deep neural networks escalates, traditional federated learning (FL) frameworks increasingly struggle since the training overhead of the full model is costly for resource-limited clients. In addition, the class imbalance among local datasets and client heterogeneity may lead to significant deterioration in learning performance. To address these challenges, we first propose a novel wireless split federated learning (SFL) framework to enhance learning efficiency and performance in resource-constrained networks, which adaptively splits the global model between the clients and server to alleviate the computation burden for clients. Then, we theoretically analyze how the client sampling and wireless network parameters impact on the convergence bound. Based on the analysis, we identify the extent of class imbalance that significantly impacts learning performance. Inspired by this, we formulate an optimization problem to strike a balance between latency and performance by jointly optimizing the client selection, model splitting, and bandwidth allocation policies. To solve this problem, we introduce a latency and class imbalance-aware double greedy algorithm to obtain client scheduling policy. Additionally, bisection-enabled optimal bandwidth allocation and model splitting algorithms are developed to adaptively determine bandwidth allocation and model splitting policies, respectively. Extensive experimental results demonstrate that our approach significantly reduces latency and enhances learning performance.