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
Keywords
Related Articles
- EP ID EP752183
- DOI 10.47852/bonviewJDSIS3202998
- Views 34
- Downloads 0
Identification of Damage in a Wind Turbine Blade Using Mechanical Measurements and Artificial Neural Networks
Due to the stochastic nature of environmental loadings, a lot of interest is paid in the discovery of possible damages to the involved equipment in modern industry. In wind turbines' blades, the development of a smart st...
Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review
In this era, feature clustering is a prominent technique in data mining. Feature clustering has also huge applications in biomedical research for multiple purposes including grouping, feature reduction, and many more. Th...
Detection of Facial Mask Using Deep Learning Classification Algorithm
Deep learning is an algorithm that works by representing data in layers of learning layers so that the representation becomes more meaningful. "Deep" in deep learning means that deep learning begins layers of sequential...
Voice Biomarkers for Parkinson's Disease Prediction Using Machine Learning Models with Improved Feature Reduction Techniques
As a chronic and life-threatening disease, Parkinson’s disease (PD) causes people to become rigid and inactive and have shaky voices. There is an argument that current PD detection techniques are ineffective due to their...
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...