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

Autor:	Victor, Nancy, C, Rajeswari., Alazab, Mamoun, Bhattacharya, Sweta, Magnusson, Sindri, Maddikunta, Praveen Kumar Reddy, Ramana, Kadiyala, Gadekallu, Thippa Reddy
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: The paper is accepted for publication in IEEE IoT Magazine
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.13976 Zobrazit plný text záznamu View this record from Arxiv