Sync-GWO: Highly Private and Bandwidth-Efficient Federated Learning With a Case Study in Healthcare.

Abstract

Federated Learning (FL) is a transformative paradigm in machine learning that enables collaborative model training across decentralized devices, ensuring data remains securely stored locally to enhance privacy. Traditional FL techniques, such as FedAvg, rely on gradient aggregation to construct a global model. However, these approaches often incur significant communication overhead and pose privacy risks, as gradient updates can inadvertently expose sensitive information. To address these challenges, this paper presents a novel FL framework that formulates federated optimization as a Multi-Objective Optimization (MOO) problem and proposes Sync-GWO, an innovative adaptation of the Grey Wolf Optimizer (GWO) tailored specifically for FL. In contrast to conventional population-based methods that require full population transfers, Sync-GWO leverages synchronized Pseudo-Random Number Generators (PRNGs) to eliminate population data transmission. The proposed approach significantly reduces communication overhead while enhancing privacy by avoiding gradient exchange. Experimental results on COVID-19 pandemic-related Internet of Medical Things (IoMT) data demonstrate that Sync-GWO achieves up to 15% higher accuracy and $2.5\times$ improved F1-scores compared to FedAvg, while reducing communication costs by over 99% to approximately 1 KB per round. Sync-GWO is particularly well-suited for scenarios requiring high privacy and extreme communication efficiency, such as those encountered in IoMT. Furthermore, Sync-GWO exhibits robust performance on imbalanced datasets and in optimization settings involving non-differentiable objectives, where gradient-based methods are often less effective.