Revolutionizing Internet of Underwater Things with Federated Learning

This chapter explores the transformative intersection of Federated Learning (FL) and the Internet of Underwater Things (IoUT), presenting a paradigm shift in how underwater things operate autonomously and efficiently. The unique challenges posed by the underwater environment necessitate innovative solutions, and FL emerges as a promising approach to address data privacy, resource constraints, and decentralized learning. The chapter delves into the integration of FL techniques, discussing their applications, benefits, and challenges in the context of IoUT. This chapter aims to contribute to the evolving literature on underwater drone technologies, providing a comprehensive overview of how Federated Learning can empower the Internet of Underwater Things to operate intelligently, autonomously, and securely in challenging underwater environments.