Federated Learning in Healthcare: A Privacy-Preserving Framework for Distributed Medical Data Analytics
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I1P101Keywords:
Federated Learning, Privacy-Preserving AI, Healthcare Data Analytics, Machine Learning, Differential Privacy, Secure Multi-Party Computation, Homomorphic Encryption, Medical Diagnosis, Distributed Computing, Predictive AnalyticsAbstract
Federated Learning (FL) is an emerging paradigm that enables the training of machine learning models across multiple decentralized devices or servers, each holding local data samples, without the need to exchange the data itself. This approach is particularly valuable in the healthcare domain, where data privacy and security are paramount. This paper explores the application of federated learning in healthcare, focusing on its potential to enhance medical data analytics while preserving patient privacy. We present a comprehensive overview of the challenges and opportunities in this domain, discuss the technical foundations of federated learning, and propose a privacy-preserving framework for distributed medical data analytics. We also evaluate the performance of our proposed framework using real-world healthcare datasets and provide insights into future research directions.
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