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@article{205447,
        author = {SARANYA DEVI .T.V and E. UVA SHAKTHI},
        title = {Federated Learning Platform for Privacy-Preserving Medical Predictions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {6603-6608},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205447},
        abstract = {Privacy concerns and healthcare regulations often prevent hospitals from sharing patient data for developing accurate machine learning models. Federated Learning (FL) provides a solution by enabling multiple healthcare institutions to collaboratively train a global model without exchanging sensitive patient information. This project presents a Federated Learning platform for privacy-preserving medical prediction, where each hospital trains a local model using its own data and shares only the model parameters with a central server. The server aggregates these updates using the Federated Averaging (FedAvg) algorithm to generate an improved global model. To further enhance privacy, Differential Privacy is applied to protect the transmitted model updates from potential information leakage. The proposed system supports secure collaboration among healthcare organizations while ensuring compliance with data privacy regulations. Experimental results demonstrate that the federated model achieves improved prediction performance compared to isolated local models, without compromising patient confidentiality. This approach enables the development of reliable medical prediction systems and promotes secure, collaborative healthcare research.},
        keywords = {Federated Learning, Medical Prediction, Differential Privacy, Privacy Preservation, Healthcare, Federated Averaging (FedAvg).},
        month = {June},
        }