Federated Learning for Energy Management in Next Generation Smart Cities

Year : 2024 | Volume :14 | Issue : 01 | Page : 19-27
By

Aiswarya Dwarampudi

Manas Kumar Yogi

  1. Assistant Professor Department of Computer Science and Engineering, Pragati Engineering College Andhra Pradesh India
  2. Assistant Professor Department of Computer Science and Engineering, Pragati Engineering College Andhra Pradesh India

Abstract

Federated learning has emerged as a promising approach for addressing the challenges of energy management in next-generation smart cities. This decentralized approach to machine learning allows collaborative model training among distributed data sources, while safeguarding data privacy and security. In this study, we explore the application of federated learning techniques to optimize energy consumption, enhance grid stability, and promote sustainability in smart city environments. By aggregating data from diverse sources such as smart meters, IoT devices, and renewable energy sources, federated learning models can analyze patterns, predict demand, and optimize energy distribution without compromising individual privacy. We discuss various federated learning algorithms and architectures tailored to energy management applications, including federated optimization, federated averaging, and differential privacy mechanisms. Furthermore, we highlight the potential benefits of federated learning in enabling real-time decision-making, reducing operational costs, and minimizing environmental impact in smart cities. Through case studies and simulations, we demonstrate the effectiveness and scalability of federated learning approaches in improving energy efficiency and grid reliability while accommodating the dynamic and heterogeneous nature of urban environments. Finally, we discuss the challenges and opportunities associated with deploying federated learning systems in real-world smart city deployments, including data heterogeneity, communication constraints, and regulatory compliance. We conclude by outlining future research directions and recommending strategies for integrating federated learning into the energy management infrastructure of next-generation smart cities.

Keywords: Federated learning, smart, energy, IoT, machine learning

[This article belongs to Journal of Communication Engineering & Systems(joces)]

How to cite this article: Aiswarya Dwarampudi, Manas Kumar Yogi. Federated Learning for Energy Management in Next Generation Smart Cities. Journal of Communication Engineering & Systems. 2024; 14(01):19-27.
How to cite this URL: Aiswarya Dwarampudi, Manas Kumar Yogi. Federated Learning for Energy Management in Next Generation Smart Cities. Journal of Communication Engineering & Systems. 2024; 14(01):19-27. Available from: https://journals.stmjournals.com/joces/article=2024/view=140048




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Regular Issue Subscription Review Article
Volume 14
Issue 01
Received March 28, 2024
Accepted March 31, 2024
Published April 5, 2024