MOBILE: Mobility and Outage-Based Intelligent Federated Learning in Mobile Computing
Qiyuan Wang, Qianyu Long, Christos B. Anagnostopoulos · Frontiers in artificial intelligence and applications · 2025
We address the challenges in Federated Learning (FL) amid client mobility by proposing MOBILE, a framework that optimizes client selection and resource allocation while ensuring sufficient client interactions per round. MOBILE leverages historical mobility data instead of focusing on conventional energy constraints, formulating client selection as a regularized Mixed-Integer Quadratic Programming (MIQP) problem. By selecting clients with higher expected returns, MOBILE identifies reliable participants while minimizing wasted bandwidth. Our framework increases participation of clients likely to succeed in training tasks and is orthogonal to existing FL algorithms, introducing minimal overhead while preserving user incentives. Experiments on benchmark mobility and FL datasets demonstrate MOBILE’s superiority in model accuracy and resource utilization compared to baseline approaches.