AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks

• URL: http://arxiv.org/abs/2403.13101v3
• Date: Wed, 22 May 2024 07:10:12 GMT
• Title: AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
• Authors: Zheng Lin, Guanqiao Qu, Wei Wei, Xianhao Chen, Kin K. Leung,
• Abstract summary: Split federated learning (SFL) is a promising solution by of floading the primary training workload to a server via model partitioning. We propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems.
• Score: 15.195798715517315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of floading the primary training workload to a server via model partitioning while enabling parallel training among edge devices. However, although system optimization substantially influences the performance of SFL under resource-constrained systems, the problem remains largely uncharted. In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation. Then, we propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls client-side MA and MS to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve a target accuracy than benchmarks, demonstrating the effectiveness of the proposed strategies.

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