Harnessing Federated Learning for Privacy-Preserving AI in Healthcare
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Abstract
Federated learning (FL) has emerged as a promising approach to enable collaborative machine learning without compromising data privacy. This paper explores the application of FL in healthcare, focusing on scenarios such as multi-hospital collaboration for disease prediction and patient monitoring. The study highlights the architecture of FL, its ability to process decentralized data securely, and its potential to improve model accuracy across diverse datasets. Challenges such as communication overhead, model convergence, and security threats are discussed. The paper concludes with recommendations for implementing FL to achieve scalable and privacy-compliant AI solutions in healthcare.
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References
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