Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{175777,
        author = {S Suneel and T. RajyaLakshmi},
        title = {Federated Learning with XGBoost and LSTM for Electric Vehicle Energy Demand Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4668-4671},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175777},
        abstract = {The growing popularity of electric vehicles (EVs) is driven by their advantages over traditional gas-powered cars, but integrating them into the power grid poses challenges like increased energy demand and peak loads. To address this, we propose a blockchain-based federated learning approach for predicting EV energy demands using linear regression algorithms. Data from EVs is securely stored on the blockchain, accessible only by authorized users. Each EV trains a machine learning model using federated learning, and the resulting model parameters are shared on the blockchain. Our approach also tackles communication delays and overheads within the blockchain-federated learning (BCFL) framework, optimizing system performance. Results demonstrate the efficiency of our method in predicting EV energy needs accurately.},
        keywords = {Electric Vehicles (EVs), Blockchain, Federated Learning, Energy Demand Prediction, Blockchain-Federated Learning (BCFL), Linear Regression, Power Grid, Machine Learning Model},
        month = {April},
        }