Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide

Abstract

Federated Learning is defined as a decentralized approach to machine learning that enables multiple devices to collaboratively train a shared model while keeping their data localized and private. This paper offers a comprehensive review of FL’s integration with the Internet of Things (IoT), serving as a guidebook for future research directions through 2033. It explores the current state-of-the-art applications of FL within IoT, emphasizing its potential to enhance critical functionalities such as secure data sharing, computational offloading, attack detection, localization, and mobile crowdsensing. The paper identifies key challenges, including resource constraints, communication efficiency, and the need for robust defenses against adversarial attacks, and proposes targeted research initiatives to address these issues. By encouraging interdisciplinary collaboration and the development of innovative algorithmic solutions, this guide outlines a clear roadmap for advancing the integration of FL within IoT, aiming to foster the creation of secure, scalable, and privacy-preserving IoT networks that will underpin the technological landscape of 2033.