Federated Learning for IoUT: Concepts, Applications, Challenges and Future Directions
Nancy Victor, Rajeswari Chengoden, Mamoun Alazab, Sweta Bhattacharya, Sindri Magnússon, Praveen Kumar Reddy Maddikunta, Kadiyala Ramana, Thippa Reddy Gadekallu
Abstract
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 ML, that can help in fulfilling the challenges faced by conventional ML approaches in IoUT. This article presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.