FEDERATED LEARNING FOR PREDICTION OF ENERGY CONSUMPTION IN EDGE-ENABLED SMART CITIES WITH HEALTHCARE INFRASTRUCTURE CROSSROADS
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Keywords
Energy Load Forecasting, Edge Computing, LSTM, Federated Learning, Smart Grid, Pharmaceutical, Healthcare Infrastructure
Abstract
The smart grid employs large amounts of consumption data to develop advanced machine learning models for various purposes, such as load monitoring and demand response. However, these applications pose security threats and demand a high level of precision. On one hand, the data used is extremely susceptible to privacy concerns. For instance, the comprehensive data collected by a smart meter installed in a consumer's home might offer valuable insights about the precise appliances being utilized and, subsequently, the consumer's behavior at home. Conversely, deep learning models require substantial amounts of varied data to be effectively taught. This study evaluates the deployment of Edge computing and federated learning, a distributed machine learning technique that allows for the increase in both the amount and diversity of data used to train deep learning models, while also preserving privacy. This work introduces the novel application of federated learning to estimate home load, a task that, as far as we know, has not been investigated before. The obtained results are promising. The simulations were performed using Federated on a dataset comprising 300 residential properties. Examining the convergence of smart grid technologies in smart cities with the pharmaceutical market and taking healthcare infrastructure considerations into account holds promise for pioneering solutions that tackle challenges across various sectors. This cooperative strategy has the potential to foster a healthcare system that is more robust, sustainable, and cost-effective, aligning seamlessly with overarching healthcare policy goals.
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Jamshaid Iqbal Janjua
Computer Sciences, National College of Business Administration & Economics, Lahore, Pakistan.
Areej Fatima
Faculty of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan.
Atifa Athar
Department of Computer Science, Comsats University Islamabad, Lahore Campus, 54000, Pakistan.
Muhammad Saleem Khan
Computer Sciences, National College of Business Administration & Economics, Lahore, Pakistan.
Zia Ul Rehman
Department of Computer Science, Government College University Lahore, 54000, Pakistan.
Rabia Javed
Department of Computer Science, TIMES Institute, Multan, Pakistan, Pakistan.
Tahir Abbas
Department of Computer Science, TIMES Institute, Multan, Pakistan, Pakistan.
Insia Zahra
Institute of Computer Science and Information Technology, The Women University, Multan, Pakistan.