Federated Learning in IoT-Driven Smart Systems: A Review of Privacy-Preserving Intelligence
Abstract
The rapid expansion of Internet of Things (IoT) devices has led to massive distributed data generation, raising significant concerns regarding data privacy and security. Federated Learning (FL) has emerged as a promising solution by enabling decentralized model training without sharing raw data. This paper presents a comprehensive review of federated learning techniques in IoT-driven smart systems, including smart healthcare, smart cities, and industrial IoT. It evaluates communication efficiency, model accuracy, and scalability challenges. The study also highlights issues such as heterogeneous data distribution, system latency, and security vulnerabilities. Future directions include adaptive federated frameworks, integration with edge AI, and robust privacy-preserving mechanisms.
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