Abstract
In medical fields, data sharing for patients can improve the collaborative diagnosis and the complexity of traditional medical treatment process. Under the condition of data supervision, federated learning breaks the restrictions between medical institutions and realizes the sharing of medical data. However, there are still some issues. For example, lack of trust among medical institutions leads to the inability to establish safe and reliable cooperation mechanisms. For another example, malicious medical institutions destroy model aggregation by sharing false parameters. In this paper, we propose a new federated learning scheme based on blockchain architecture for medical data sharing. Moreover, we propose an intelligent contract to verify the identity of participants and detect malicious participants in federated learning. The experimental results show that the proposed data sharing scheme provides a credible participation mechanism for medical data sharing based on federal learning, and provides both higher efficiency and lower energy consumption as well.
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Acknowledgments
This work is partially supported by National Key R&D Program of China (Grant No. 2019YFB2102600), NSFC (Grants No. 61832012, 61771289), the Key Research and Development Program of Shandong Province (Grant No. 2019JZZY020124), the Pilot Project for Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) (Grant No. 2020KJC-ZD02), and the Key Program of Science and Technology of Shandong (Grant No. 2020CXGC010901).
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Wang, Z., Yan, B., Yao, Y. (2021). Blockchain Empowered Federated Learning for Medical Data Sharing Model. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_57
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DOI: https://doi.org/10.1007/978-3-030-86137-7_57
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