Delay Minimization of Federated Learning Over Wireless Powered Communication Networks

Abstract

In this paper, we study distributed federated learning (FL) in wireless powered communication networks (WPCNs). The proposed system model ensures data privacy and energy self-sustainability of wireless (e.g., sensory, sensing or data gathering) devices involved in collaborative machine learning regardless of the specific FL algorithm. We specifically aim to minimize the total training duration of the FL process by properly allocating the communication resources (i.e., durations of energy harvesting, local processing and transmission phases, and transmit powers), the computational parameters of the EH clients (i.e. CPU frequencies) and learning parameters of their FL models (i.e. local training error threshold). We derive analytical solutions for these parameters, resulting in low complexity in implementing the proposed scheme.