【Author】
Wang, Zexin; Yan, Biwei; Yao, Yan
【Source】WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2021, PT III
【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.
【Keywords】Blockchain; Federated learning; Medical data sharing; Smart contract
【摘要】在医疗领域，患者的数据共享可以改善协同诊断和传统医疗过程的复杂性。在数据监管的情况下，联邦学习打破了医疗机构之间的限制，实现了医疗数据的共享。但是，仍然存在一些问题。例如，医疗机构之间缺乏信任，导致无法建立安全可靠的合作机制。再比如，恶意医疗机构通过共享虚假参数来破坏模型聚合。在本文中，我们提出了一种新的基于区块链架构的联邦学习方案，用于医疗数据共享。此外，我们提出了一种智能合约来验证参与者的身份并检测联邦学习中的恶意参与者。实验结果表明，所提出的数据共享方案为基于联邦学习的医疗数据共享提供了一种可信的参与机制，同时提供了更高的效率和更低的能耗。
【关键词】区块链；联邦学习；医疗数据共享；智能合约
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