Federated Learning for Privacy-Preserving Edge IoT Networks

Abstract

The rapid expansion of Internet of Things (IoT) devices has created massive amounts of distributed data generated at the network edge. Traditional cloudbased machine learning requires sending raw data from devices to centralized servers, which often leads to privacy risks, communication overhead, and latency issues. Federated Learning (FL) has emerged as a promising solution that allows collaborative model training without sharing raw data. In this approach, edge devices train models locally and only model updates are communicated to a central aggregator. This significantly improves data privacy while reducing bandwidth usage. This paper reviews the concept of federated learning and its application in privacy-preserving edge IoT networks. The study discusses the architecture of FL-enabled IoT systems, working mechanism, communication models, advantages, and practical applications in healthcare, smart cities, and industrial IoT. Challenges such as communication cost, device heterogeneity, security vulnerabilities, and model convergence are also highlighted. In addition, recent research developments and possible future directions are analyzed. The review shows that federated learning can be an effective approach for building intelligent and secure edge IoT systems while maintaining user privacy.

KEYWORDS: Federated Learning, Edge Computing, Internet of Things, Privacy Preservation, Distributed Machine Learning, Edge Intelligence