Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities

• URL: http://arxiv.org/abs/2207.13976v1
• Date: Thu, 28 Jul 2022 09:40:25 GMT
• Title: Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities
• Authors: Nancy Victor, Rajeswari. C, Mamoun Alazab, Sweta Bhattacharya, Sindri Magnusson, Praveen Kumar Reddy Maddikunta, Kadiyala Ramana, Thippa Reddy Gadekallu
• Abstract summary: 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. This paper presents an overview of the various applications of Federated learning (FL) in IoUT, its challenges, open issues and indicates direction of future research prospects.
• Score: 9.705327282988916
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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