Powering the Future of IoT: Federated Learning for Optimized Power Consumption and Enhanced Privacy

TL;DR

This paper discusses how Federated Learning can improve power efficiency and privacy in IoT systems, emphasizing its potential to make IoT devices more sustainable and secure while addressing current challenges.

Contribution

It provides a comprehensive analysis of Federated Learning's role in enhancing IoT sustainability, privacy, and security, highlighting opportunities and limitations for future research.

Findings

FL can significantly reduce power consumption in IoT devices.

FL enhances privacy and security in IoT data processing.

Challenges remain in implementing FL effectively in IoT environments.

Abstract

The widespread use of the Internet of Things has led to the development of large amounts of perception data, making it necessary to develop effective and scalable data analysis tools. Federated Learning emerges as a promising paradigm to address the inherent challenges of power consumption and data privacy in IoT environments. This paper explores the transformative potential of FL in enhancing the longevity of IoT devices by mitigating power consumption and enhancing privacy and security measures. We delve into the intricacies of FL, elucidating its components and applications within IoT ecosystems. Additionally, we discuss the critical characteristics and challenges of IoT, highlighting the need for such machine learning solutions in processing perception data. While FL introduces many benefits for IoT sustainability, it also has limitations. Through a comprehensive discussion and analysis, this paper elucidates the opportunities and constraints of FL in shaping the future of sustainable and secure IoT systems. Our findings highlight the importance of developing new approaches and conducting additional research to maximise the benefits of FL in creating a secure and privacy-focused IoT environment.