FEDERATED LEARNING IN SMART METERS

In this paper, we propose a software-based FL framework for privacy-preserving smart meter data analytics and energy consumption prediction. The proposed framework overcomes the major drawbacks of the traditional centralized learning approach, i.e., the risk of data privacy, communication costs, and scalability issues. The proposed framework allows the training of the model on multiple local data sources through the distributed approach, thus decentralizing the smart meter data while using the FedAvg for global model update. The framework incorporates the integration of various machine learning algorithms such as Random Forest, Gradient Boosting, and Long Short-Term Memory to effectively model the static and dynamic characteristics of smart meter data. Experimental evaluation of the framework using synthetic data demonstrated promising accuracy of the federated models over the centralized learning models with improved data confidentiality and communication efficiency. The paper points out the very reasons that make Federated Learning a game-changing methodology in the development of secure, distributed, and intelligent energy management systems. The scalability of the deployment is feasible with the architecture, which makes it possible for different organizations to contribute to the improvement of the collaborative model without compromising privacy. The modular structure of the framework makes it possible to incorporate different algorithms and data, making the framework highly adaptable to different applications in energy analytics.

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