Communication-efficient federated learning for edge IoT via combined quantization and sparsification
Edge IoT devices are increasingly targeted for on-device intelligence using federated learning (FL). However, conventional FL imposes heavy communication and energy costs that make it impractical for battery-constrained, bandwidth-limited deployments with heterogeneous (non-IID) data. In this paper we present a practical, communication-efficient FL framework that combines update quantization with top-k sparsification and evaluates its performance under realistic edge conditions. We implement the framework in MATLAB and conduct an empirical study on EMNIST and a synthetic IoT sensor dataset across varied Dirichlet non-IID severities, client dropout rates, and multiple random seeds. Our experiments show that combining low-bit quantization (4 bits) with sparsification (top 2–5%) yields large reductions in transmitted bytes and estimated uplink energy while maintaining useful model fidelity: Compared with uncompressed FedAvg, the proposed 4-bit quantization and top-1% sparsification with error feedback reduces cumulative communication by approximately 98.87%, while achieving a final accuracy of 70.30% ± 0.40% across three random seeds. We provide detailed convergence analysis, confidence intervals across seeds, and an energy model translating bytes to Joules to quantify device-level savings. Finally, we analyze failure modes and robustness under extreme heterogeneity and client dropout, and provide reproducible MATLAB code and result artifacts.