Performance Optimization of Split Federated Learning in Heterogeneous Edge Computing Environments

Clients in federated learning (FL) may exhibit varying computing capabilities, leading to prolonged training latency when deploying complex deep neural networks. To address this challenge, split federated learning (SFL) presents an approach that offloads the main computational workload from resource-constrained devices to a server, while enabling parallel training. However, there are two significant limitations of existing SFL frameworks: The adoption of a uniform cut layer strategy fails to take into account the heterogeneous among clients; it fails to effectively utilize server-side resources to improve training efficiency. This article presents a framework, i.e., heterogeneous split federated learning, which considers personalized cut layer selection and server resource configuration to accelerate SFL in heterogeneous edge computing environments. By splitting the global model into two components for each client, our framework jointly optimizes both client-side workload, batch size control, and server resource configuration strategy, while considering device heterogeneity. Specifically, we develop an alternating iterative scheduling algorithm to obtain an approximate scheme for the cut layer, batch sizes, and server resource configuration to alleviate the impact of device heterogeneity. The experimental results illustrate that HSFL outperforms the compared methods, achieving performance improvements of up to 3.9% $\sim$ 32.2% across two datasets under various data distribution scenarios, which demonstrates the effectiveness of the proposed strategies.