Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities
This is an incremental review paper that discusses applying federated learning to IoUT for improved privacy in applications like environmental monitoring and defense.
The paper addresses data privacy and security challenges in Internet of Underwater Things (IoUT) systems by proposing federated learning as a decentralized solution, but it does not present new experimental results or concrete numbers.
Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in machine learning, that will help in fulfilling the challenges faced by conventional ML approaches in IoUT. This paper presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.