Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning

arXiv:2311.13163v2 Announce Type: replace
Abstract: Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency. To address these issues, this paper presents a novel SFL approach, named Sliding Split Federated Learning (S$^2$FL), which adopts an adaptive sliding model split strategy and a data balance-based training mechanism. By dynamically dispatching different model portions to AIoT …

abstract accuracy advantages aiot arxiv cake challenges cs.dc cs.lg data efficiency federated learning however inference low split systems type