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@article{206689,
        author = {Shrinidhi Anchan and Prajwal K S and Shishir R Kulal and Swasthik Rai and Saket L Kumbla},
        title = {Federated Learning for Privacy-Preserving Healthcare Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {219-223},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206689},
        abstract = {The healthcare industry deals with large amounts of confidential patient information which can enhance the performance of machine learning algorithms to diagnose diseases and provide better treatments. Due to privacy and ethical issues, the sharing of such data is not possible. In recent times, Federated Learning (FL) has emerged as an excellent framework for training models in a cooperative manner without using the actual data. This work provides a review of FL in healthcare along with its problems and future research opportunities.},
        keywords = {Communication Overhead, Data Heterogeneity, Deep Leakage from Gradients, Federated Learning, Privacy-Preserving Healthcare.},
        month = {July},
        }