Hierarchical Split Federated Learning: Convergence Analysis and System Optimization
This work addresses scalability issues in federated learning for edge computing, but it is incremental as it extends prior split federated learning methods to multi-tier architectures.
The paper tackles the challenge of deploying large AI models on resource-constrained edge devices in federated learning by proposing a hierarchical split federated learning framework, and it demonstrates through simulations that an optimization algorithm effectively improves model splitting and aggregation in multi-tier systems.
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.