Academic Journal
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
| Title: | Hierarchical Split Federated Learning: Convergence Analysis and System Optimization |
|---|---|
| Authors: | Zheng Lin, Wei Wei, Zhe Chen, Chan-Tong Lam, Xianhao Chen, Yue Gao, Jun Luo |
| Source: | IEEE Transactions on Mobile Computing. 24:9352-9367 |
| Publication Status: | Preprint |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Computer Science - Networking and Internet Architecture, Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Artificial Intelligence, Distributed, Parallel, and Cluster Computing (cs.DC), Machine Learning (cs.LG) |
| Description: | As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent years. Nevertheless, most prior works on SFL focus only on a two-tier architecture without harnessing multi-tier cloudedge computing resources. In this paper, we intend to analyze and optimize the learning performance of SFL under multi-tier systems. Specifically, we propose the hierarchical SFL (HSFL) framework and derive its convergence bound. Based on the theoretical results, we formulate a joint optimization problem for model splitting (MS) and model aggregation (MA). To solve this rather hard problem, we then decompose it into MS and MA subproblems that can be solved via an iterative descending algorithm. Simulation results demonstrate that the tailored algorithm can effectively optimize MS and MA for SFL within virtually any multi-tier system. 15 pages, 9 figures |
| Document Type: | Article |
| ISSN: | 2161-9875 1536-1233 |
| DOI: | 10.1109/tmc.2025.3565509 |
| DOI: | 10.48550/arxiv.2412.07197 |
| Access URL: | http://arxiv.org/abs/2412.07197 |
| Rights: | IEEE Copyright arXiv Non-Exclusive Distribution |
| Accession Number: | edsair.doi.dedup.....c9db36c00f571c403ea9c480afd04b55 |
| Database: | OpenAIRE |
| ISSN: | 21619875 15361233 |
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| DOI: | 10.1109/tmc.2025.3565509 |