Age of Information Based Client Selection for Wireless Federated Learning With Diversified Learning Capabilities

Abstract

Federated Learning (FL) empowers wireless intelligent applications, by leveraging distributed data of edge clients for training without compromising privacy. Client selection is inevitable in FL, since clients have diversified learning capabilities arising from heterogeneous computing and communication resources. Existing methods like fair-selection and dropping-straggler are either inefficient or unfair (resulting in a less effective trained model). Therefore, we propose FedAoI, an Age-of-Information (AoI) based client selection policy. FedAoI ensures fairness by allowing all clients, including stragglers, to submit their model updates while maintaining high training efficiency by keeping round completion times short. This trade-off is achieved by minimizing Peak-AoI (PAoI), the interval between a client's consecutive participations. An optimization problem is formulated by minimizing the Expected-Weighted-Sum-of-PAoI. This NP-hard problem is addressed with a two-step sub-optimal algorithm, PriorS. It first calculates client priority in a round using Lyapunov optimization and then selects the highest-priority clients through G-FPFC (Greedy minimization of the round weighted-sum-of-PAoI with First-Priority-First-Considered). Simulation results demonstrate that, compared to fair-selection, FedAoI improves average efficiency by 83.8% and achieves an average model accuracy of 97.3% (or at the cost of averaging 2.7% degradation in model accuracy). Compared to dropping-straggler, FedAoI reduces the average model accuracy degradation from 9.5% to 2.7%.