Energy-Efficient Resource Management in IoT Networks through Federated Learning

Abstract

The widespread expansion of Internet of Things networks poses inconvenient power consumption challenges and demands better resource utilization since the regular IoT devices have low power supplies and processing power. The traditional systems are not effective in a centralized system as they bring about a lot of communication issues as well as the threat to privacy and less scalability. One of the suggested approaches to enhancing the efficiency of the IoT network in terms of energy consumption and appropriate performance results is a FL-based optimization framework. The suggested solution applies edge computer systems and local data processing that reduces information transfer costs as well as amounts of power consumption. The study is aimed at creating an energy-efficient predictive resource management model that integrates FL lightweight algorithms and optimization techniques to optimize the factors of performance of the IoT network in real-time, such as power consumption and command speed and rational processing. The study will be done by simulation analysis using real world data and federated architectural systems to ensure its performance standards. The proposed achievement in the form of an intelligent sustainable IoT ecosystem that does not require the human touch to function efficiently is there.