HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
This work addresses performance bottlenecks in edge computing systems for machine learning applications, representing an incremental improvement over existing split federated learning methods.
The paper tackles the straggler effect in split federated learning caused by heterogeneous edge devices by proposing HASFL, which adaptively controls batch sizes and model splitting to balance latency and convergence, achieving superior performance over state-of-the-art benchmarks in experiments.
Split federated learning (SFL) has emerged as a promising paradigm to democratize machine learning (ML) on edge devices by enabling layer-wise model partitioning. However, existing SFL approaches suffer significantly from the straggler effect due to the heterogeneous capabilities of edge devices. To address the fundamental challenge, we propose adaptively controlling batch sizes (BSs) and model splitting (MS) for edge devices to overcome resource heterogeneity. We first derive a tight convergence bound of SFL that quantifies the impact of varied BSs and MS on learning performance. Based on the convergence bound, we propose HASFL, a heterogeneity-aware SFL framework capable of adaptively controlling BS and MS to balance communication-computing latency and training convergence in heterogeneous edge networks. Extensive experiments with various datasets validate the effectiveness of HASFL and demonstrate its superiority over state-of-the-art benchmarks.