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.

通过模型分割和客户端模型聚合，我们提出了一种适应资源受限边缘计算系统的新型资源自适应分割联邦学习（AdaptSFL）框架，以加速分割联邦学习并实现更好的训练性能。