Authors : P. Uma Shankar; E. Surya Rahul; K. Durga Rao; K. Satish; U. Dayanand Kumar; D. Ravindra; G. Subbarao

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/4dn778wz

Scribd : https://tinyurl.com/4m7azuph

DOI : https://doi.org/10.38124/ijisrt/25apr626

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Abstract : Developing a model through a centralized approach, where data is shared among all stakeholders, enhances its reliability. However, privacy concerns—especially regarding medical datasets—often impede data sharing. Numerous machine learning models have been created using isolated datasets, leading to challenges with overfitting and poor performance on new datasets. Consequently, there is an urgent need to create a model that achieves accuracy comparable to centralized models while upholding security standards. Efficient diagnosis of liver disease typically depends on analyzing imaging techniques such as CT and MRI scans. Traditional machine learning methods face difficulties due to the decentralized nature of medical data across institutions, which is further complicated by stringent privacy regulations. Federated learning offers a solution by enabling local model training, allowing institutions to collaborate without exchanging raw data; instead, they share only model updates. This approach safeguards data privacy while enhancing model reliability.

Keywords : Federated Learning, Decentralized Model, Model Aggregation, privacy preservation, Medical Imaging, Collaborative Training.

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