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

arXiv:2403.13101v1 Announce Type: new
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 …

abstract arxiv challenge complexity cs.ai cs.dc cs.lg devices edge edge devices edge networks enabling federated learning networks neural networks partitioning server solution them training type via