Federated Learning in Distributed Systems: A Privacy-First Approach

Abstract

Federated learning has emerged as a transformative approach in machine learning, addressing critical challenges in data privacy and distributed computation. This article examines the evolution and implementation of federated learning across various sectors, focusing on its impact in healthcare, smart cities, and enterprise applications. The article analyzes the core principles of decentralized model training, advanced privacy-preserving techniques, and real-world applications. Through detailed examination of secure aggregation protocols, differential privacy mechanisms, and homomorphic encryption integration, this article demonstrates the effectiveness of federated learning in maintaining data privacy while achieving competitive model performance. The article highlights significant advancements in healthcare analytics, particularly in medical imaging and personalized treatment optimization, as well as substantial improvements in smart city infrastructure management. This article contributes to the understanding of federated learning's practical implementation challenges and solutions, providing insights into future directions for privacy-preserving distributed machine learning.