提出分层联邦学习框架，优化多级系统中的模型分割与聚合。
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
- 设计分层架构，将模型分割与聚合协同优化
- 理论推导收敛边界，指导算法设计
- 适用于资源受限的多级边缘计算场景
随着人工智能模型规模扩大，将联邦学习（FL）部署在资源受限的边缘设备上变得愈发困难。为应对这一挑战，模型分割联邦学习（SFL）通过将模型拆分以降低边缘设备负载而受到广泛关注。然而，现有大多数SFL研究仅关注两级架构，未能充分利用多级云-边计算资源。本文旨在分析并优化多级系统下的SFL学习性能。我们提出分层分裂联邦学习（HSFL）框架，并推导其收敛界。基于理论结果，构建了联合优化问题，用于模型分割（MS）与模型聚合（MA）。针对该难题，我们将其分解为可迭代求解的子问题，并设计下降算法。仿真结果表明，所提算法能在几乎任意多级系统中有效优化MS与MA。
原文摘要 · Abstract (English)
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.
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