Design and Implementation of a Blockchain-Assisted Federated Learning Framework for Privacy-Preserving Healthcare Data Analytics
DOI:
https://doi.org/10.64137/XXXXXXXX/IJMST-V1I1P103Keywords:
Blockchain, Federated learning, Privacy-preserving, Healthcare analytics, Bias mitigation, Fairness, Distributed architecture, Medical data, Transparency, SecurityAbstract
The use of both federated learning and blockchain ensures better privacy for healthcare data analysis. Traditional approaches to machine learning in healthcare are affected by risks related to data breaches, data privacy, and having data from several sources that may be biased. In federated learning, several medical institutions join forces in training data without giving away patients’ private details. Even so, there are problems that FL has to face, such as hidden practices, the possibility of bad quality in training, and the struggle to ensure trust and justice for all participants. Because of these obstacles, a blockchain-enabled federated learning platform for healthcare analytics is presented in this paper. The use of blockchain allows the framework to guarantee clear, unchanged, and trackable model updates, along with effective administration of members and encouragement for honest teamwork among them. The architecture design involves incorporating bias-reduction measures in the FL process, relying on blockchain to gather and study models, encourage fairness, and identify any dishonest or malicious parties. Simulated healthcare settings have proven that the framework increases the precision and fairness of models and significantly cuts down the chances of private information being leaked by putting parameters under decentralized control. Because the system resists single issues of failure, meets different needs, and is scalable, healthcare analytics benefit from it, and this helps improve fairness and quality of healthcare. Advanced ways of motivating users and using new technologies, such as edge computing, could be added in the future to protect privacy and enhance performance
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