Group-based Federated Learning with Cost-efficient Sampling Mechanism in Mobile Edge Computing Networks

Federated learning (FL) that preserves privacy has appeared as a prospective paradigm in mobile edge computing networks. However, due to the system and data heterogeneity of mobile clients (MCs), group-based FL with a sampling mechanism is crucial for minimizing model training costs. To address these challenges, we investigate and formulate the problem of group-based FL with a sampling mechanism for reducing model training cost (i.e., latency and energy consumption), and propose a group-based FL with a cost-efficient sampling mechanism (GFLCSM) framework to address it. More precisely, before training, each MC locally pre-trains a model, estimates its data distribution from the classifier's gradient norms, and uploads it to the central server (CS) instead of raw data to preserve privacy. Using this information, the CS transforms vanilla FL into a group-based FL. During training, GFLCSM replaces the random sampling mechanism with a cost-efficient one. Moreover, to enhance robustness against network dynamics, we extend GFLCSM with a backup resampling mechanism, termed GFLCSM-E. Experimental results indicate that GFLCSM surpasses the baseline frameworks, reducing latency by 24.63% and energy consumption by 11.47% on average across two datasets, while GFLCSM-E maintains high performance even under client dropout. The source code address is https://github.com/kt4ngw/GFLCSM.