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Aiswarya Dwarampudi, Manas Kumar Yogi
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- Assistant Professor, Assistant Professor, Department of Computer Science and engineering, Pragati Engineering College, Department of Computer Science and engineering, Pragati Engineering College, Andhra Pradesh, Andhra Pradesh, India, India
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Abstract
nFederated 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 paper, 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 analyse 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.
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Keywords: Federated Learning, Smart, Energy, IoT, Machine Learning
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References
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| Volume | 14 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | March 28, 2024 | |
| Accepted | March 31, 2024 | |
| Published | April 5, 2024 |
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