Green Federated Learning via Energy-Aware Client Selection

Abstract

Federated learning (FL) is a collaborative machine learning framework to enable different clients such as Internet of Things (IoT) devices to participate in a machine learning model training process, while preserving data privacy. Client selection is critical to determine the performance of FL. Most of the existing client selection methods aim to maximize the number of selected clients, who can upload their local models before the deadline, in each global iteration, thus potentially accelerating the model convergence rate. However, these methods ignore the fact that most of the IoT devices are powered by on-board batteries and harvested green energy from the environment to prolong battery life. Hence, clients selected by these methods may not have sufficient energy to upload their local models in a global iteration or are unable to participate in the training process in the near future due to battery drainage. In this paper, we propose a novel client selection, entitled “EnerGy-AwaRe CliEnt SElection for Green FeDerated Learning (GREED)”, to optimize the trade-off between maximizing the number of selected clients and minimizing the energy drawn from batteries for the selected clients, while ensuring that all the selected clients have sufficient energy to upload their local models before the deadline. The performance of GREED is validated via extensive simulations.