Drift-Aware Straggler Mitigation for Heterogeneous Split Federated Learning via Proactive Cut-Layer and Asynchronous Server Updates
Split Federated Learning (SFL) alleviates the computation burden on resource-constrained clients by partitioning model training between clients and the server. However, conventional synchronous SFL is highly vulnerable to straggler clients under heterogeneous compute, network, and data conditions, leading to prolonged training time and degraded system efficiency. To address this issue, we propose DAPS-SFL, a drift-aware and proactive straggler mitigation framework for heterogeneous SFL. The proposed method integrates three components: a lightweight straggler risk predictor based on normalized runtime statistics, a per-client adaptive cut-layer policy that dynamically adjusts the split point according to estimated computation and communication costs, and a drift-aware asynchronous server update mechanism that jointly accounts for update staleness and distribution shift. Experiments on CIFAR-10 with 50 clients under Dirichlet non-IID settings and heterogeneous compute/network environments demonstrate that DAPS-SFL reduces the wall-clock time required to reach 70% accuracy by approximately 22% compared with synchronous SplitFed, improves final test accuracy to 76.8 ± 0.4%, and significantly lowers tail latency while incurring only modest communication overhead. These results indicate that jointly optimizing cut-layer adaptation and asynchronous aggregation is an effective direction for improving the robustness and efficiency of SFL in heterogeneous environments.