A Contextual Client Selection Method for Volatile Federated Learning

Client selection is a promising approach to address heterogeneity in Federated learning. Existing methods predominantly focus on optimizing training efficiency, often neglecting the impact of selection fairness. In this paper, we investigate client selection in volatile environments, leveraging the Age-of-Information concept to formulate long-term fairness constraints and develop an optimization model that ensures fairness while maximizing overall efficiency. By converting the offline problem into an online framework via Lyapunov optimization, we reframe client selection under the Contextual Combinatorial Multi-Armed Bandit (C<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>MAB) framework. Integrated with the LinUCB algorithm, we propose FedECS, an efficient client selection strategy. Experiments on public datasets demonstrate FedECS’s superior fairness guarantees, achieving 13% higher accuracy than FedCS and 5.8% improvement over RBCS-F.