HASFL: Heterogeneity-aware split federated learning over edge computing systems
Author Identifier (ORCID)
Abstract
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.
Keywords
batch size, federated learning, mobile edge computing, model splitting, split federated learning
Document Type
Journal Article
Date of Publication
8-1-2026
ISSN
15361233
Volume
25
Issue
8
Publication Title
IEEE Transactions on Mobile Computing
Publisher
IEEE
School
School of Engineering
Funding Information
Research Grants Council of Hong Kong (Grant Number: 27213824 and CRS HKU702/24)
Copyright
subscription content
Content Type
Metadata only
First Page
12455
Last Page
12471
Recommended Citation
Lin, Z., Chen, Z., Chen, X., Ni, W., & Gao, Y. (2026). HASFL: Heterogeneity-aware split federated learning over edge computing systems. IEEE Transactions on Mobile Computing, 25(8), 12455–12471. https://doi.org/10.1109/TMC.2026.3673358