Efficient and Intelligent Multijob Federated Learning in Wireless Networks

Federated learning (FL) has emerged as an innovative paradigm designed to protect privacy by enabling collaborative machine learning (ML) model training across multiple data owners (also known as clients) without the need to access clients’ raw data. The majority of existing FL research concentrates on scenarios where a single job necessitates training. In practical applications, multiple FL jobs can simultaneously undergo training using a common pool of clients, a scenario known as multijob FL. However, the problem of FL training with multiple jobs remains open and presents significant challenges of the escalated heterogeneity of jobs and clients, complex tradeoffs between training latency and energy consumption, uncertainty of client quality, and potential linear switching cost associated with client selection. This work aims to jointly optimize training efficiency in terms of latency, energy consumption, and switching cost for multiple jobs in stochastic and dynamic environments. Specifically, we propose a novel multijob FL framework, named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EffI-FL</monospace>, incorporating three innovative designs: 1) to reduce switching cost, we extend the client selection interval from every round to multiple rounds, called a block, within which client subset switching is prohibited; 2) we employ multiarmed bandit (MAB) methods to measure clients’ latency and energy cost under uncertainty. Additionally, we utilize the virtual queue technique to trace clients’ battery usage patterns. By integrating the above client-side knowledge, we propose an adaptive client selection policy aimed at balancing latency, energy consumption, and battery condition; and 3) given that multiple jobs may compete for the same client, we devise a greedy algorithm to assign each client to a single job. We rigorously prove that the regret of our client selection policy and the cost of our block-wise client subset switching algorithm are both sublinear. Finally, we implement <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EffI-FL</monospace> using PyTorch and conduct experiments demonstrating that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EffI-FL</monospace> reduces the weighted sum of latency, energy consumption, and switching cost by up to 52.3% compared to four state-of-the-art FL frameworks.