Federated Learning in Edge Communication Systems: Enhancing Privacy and Efficiency in IoT Networks

Abstract

The rapid evolution of the Internet of Things (IoT) has led to the generation of vast amounts of sensitive data, driving the need for efficient and privacy-preserving machine learning models. Federated Learning (FL) has emerged as a transformative approach that allows decentralized model training across multiple edge devices without sharing raw data. This paper explores the integration of FL in edge communication systems, emphasizing its role in enhancing privacy, bandwidth optimization, and computational efficiency in IoT networks. The research investigates key components, architectures, and protocols of FL, analyzes performance metrics, and compares centralized versus federated frameworks through case-based evaluations. The study concludes that FL, coupled with edge computing, presents a sustainable paradigm for data-driven intelligence in IoT, balancing data security, latency reduction, and real-time adaptability.