【Author】
Chang, Yuxia; Fang, Chen; Sun, Wenzhuo
【Source】COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
【Abstract】The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.
【摘要】人工智能的发展和世界范围内的疫情事件推动了智慧医疗的落地，同时也带来了数据隐私、恶意攻击、服务质量等问题。医疗物联网（MIoT）以及联邦学习和区块链技术已成为解决这些问题的可行方案。在本文中，我们提出了一种基于区块链的智能医疗联邦学习方法，其中边缘节点维护区块链以抵抗单点故障，MIoT设备实施联邦学习以充分利用分布式临床数据。特别是，我们设计了一种自适应差分隐私算法来保护数据隐私和基于梯度验证的共识协议来检测中毒攻击。我们将我们的方法与现实世界糖尿病数据集上的两种类似方法进行比较。有希望的实验结果表明，我们的方法可以在可接受的运行时间内实现较高的模型精度，同时在降低隐私预算消耗和抵抗中毒攻击方面也表现出良好的性能。
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