Enhancing Privacy and Efficiency in IoT through Federated Learning

Federated Learning (FL) has emerged as a promising solution for training machine learning models across distributed devices while preserving data privacy.In the context of the Internet of Things (IoT), FL enables numerous smart devices to collaboratively learn a shared model without sharing their raw data, thus enhancing privacy and security.This paper presents an extensive and systematic review of the current state of FL in IoT environments.We explore the foundational concepts, review recent advancements, and analyze the existing systems.Furthermore, we propose a novel system that integrates Adaptive Federated Averaging (Adaptive-FedAvg), Hierarchical Federated Learning, and Enhanced Secure Model Aggregation to address the challenges of data heterogeneity, communication efficiency, and security in IoT networks.Comparative numerical analysis demonstrates that our proposed system achieves higher model accuracy, faster convergence, reduced communication overhead, and enhanced privacy protection compared to traditional FL systems.