Federated-Based Deep Reinforcement Learning (Fed-DRL) for Energy Management in a Distributive Wireless Network
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 2
Abstract
Studies on developing future generation wireless systems are expected to support increased infrastructure development and device subscriptions with densely deployed base stations (BSs). Economically, decreasing BS energy consumption levels and achieving "greenness" remain key factors for the giant industry. Some research works have proposed deep reinforcement techniques to solve energy management (EM) issues in cellular networks. However, these techniques are inefficient in a distributive network environment and expose the devices to privacy issues. Federated learning (FL) is proven to enforce device privacy and train models distributively. Thus, this work proposes an autonomous switching mode framework for BSs based on federated-deep reinforcement learning (Fed-DRL) to address the aforementioned challenges encountered by prior studies. Specifically, we deploy multiple DRL agents to influence the decision of the BS for EM. On the other hand, to make DRL-based decisions feasible and satisfy device quality-of-service, we train the DRL agents distributively by employing the FL concept. The results show the effectiveness of our proposed framework under distributed network scenarios compared with other benchmark algorithms.
Authors and Affiliations
Victor Kwaku Agbesi, Noble Arden Kuadey, Collinson Colin M. Agbesi, Gerald Tietaa Maale
Topological Data Analysis of COVID-19 Using Artificial Intelligence and Machine Learning Techniques in Big Datasets of Hausdorff Spaces
In this paper, we carry out an in-depth topological data analysis (TDA) of COVID-19 pandemic using artificial intelligence (AI) and Machine Learning (ML) techniques. We show the distribution patterns of pandemic all over...
Identifying Risk Factors for Heart Failure: A Case Study Employing Data Mining Algorithms
Heart diseases are increasingly present in the lives of human beings and are diseases that affect the heart and blood vessels and can lead the person who develops to death. In this article, we analyzed an open and public...
Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches
Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring...
An Experimental Private Small Hydropower Plant Investments Selection Classification System
Investment selection problems and models are crucial for humans, communities, and states. Private small hydroelectric power/ hydropower plant investments (PSHPPIs) selection problem is a unique one in those problems and...
Random Forest Ensemble Machine Learning Model for Early Detection and Prediction of Weight Category
The number of insurgents in our nation today is significantly rising each day, and the majority of those affected are living as internally displaced persons (IDP) in various IDP camps. These people experience a variety o...