ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices

arXiv:2402.15903v1 Announce Type: new
Abstract: Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). …

abstract arxiv cs.ai cs.lg cs.ni data devices distributed federated learning machine machine learning machine learning model multiple paper parties raw resources server train type wireless