Block chain based federated learning with SMPC Model Verification for Healthcare Systems
Keywords:
Blockchain, Federated Learning, Secure Multi-Party Computation, Healthcare, Privacy, Decentralization, Model Verification.Abstract
With the exponential growth of healthcare data, privacy and security concerns have become paramount in medical data analysis and AI-driven diagnostics. Blockchain technology offers a decentralized and immutable ledger, while Federated Learning (FL) enables collaborative model training without exposing sensitive patient data. Secure Multi-Party Computation (SMPC) further enhances privacy by ensuring computations on encrypted data without revealing raw information.
References
Ahmed, B., et al. (2020). "Blockchain for Healthcare: Security and Privacy Perspectives." IEEE Transactions on Blockchain. 2. Kairouz, P., et al. (2019). "Advances and Open Problems in Federated Learning." Journal of Machine Learning Research.
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Published
2024-09-10
How to Cite
M.Ramasubramanian, D.Himaja,D.Anitha, M.Shivani, U.Keerthi. (2024). Block chain based federated learning with SMPC Model Verification for Healthcare Systems. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 1149–1154. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1869
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