FEDERATED LEARNING: METHODOLOGY, IMPLEMENTATION, AND RESULTS

Abstract

Federated Learning (FL) is a decentralized machine learning paradigm that enables model training across multiple devices or servers while keeping data localized. This approach addresses privacy concerns, reduces communication overhead, and enhances scalability. This paper presents a comprehensive study of FL, including its methodology, key algorithms, implementation strategies, and experimental results. We evaluate FL performance on benchmark datasets, comparing it with centralized learning approaches in terms of accuracy, communication efficiency, and privacy preservation. Our findings demonstrate that FL achieves competitive performance while maintaining data privacy.