Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningFederated LearningSimilar Papers 제목 키워드 기반
Towards the Internet of Underground Things: A Systematic Survey
This paper provides recent advances in the area of Internet of Underground Things (IoUT) with emphasis on enabling communication technologies, networking issues, and localization techniques. IoUT is enabled by undergroun…
SandSurveyFederated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges
Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical mach…
Federated LearningA Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions
Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial int…
Drug DiscoveryEdge-computingFederated LearningManagement+1Towards Sustainable Internet of Underwater Things: UAV-aided Energy Efficient Wake-up Solutions
With the advancements in underwater wireless communications, internet of underwater things (IoUT) realization is inevitable to enable many practical applications, such as exploring ocean resources, ocean monitoring, unde…
An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems
Multi-agent AI systems (MAS) offer a promising framework for distributed intelligence, enabling collaborative reasoning, planning, and decision-making across autonomous agents. This paper provides a systematic outlook on…
Decision Making