WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling

arXiv:2405.00885v1 Announce Type: new
Abstract: As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating devices' computing and communication heterogeneity. Some pioneering research efforts proposed to extract subnetworks from the global model, and assign as large a subnetwork as possible to the device for local training based on its full computing and communications capacity. Although such fixed size subnetwork assignment enables FL training over heterogeneous mobile devices, …

abstract applications arxiv communication computing cs.lg cs.ni deployment devices distributed distributed learning eess.iv extract federated learning global latency mobile mobile devices paradigm popular practical research scheduling type via while wireless