【Author】
Polap, Dawid; Srivastava, Gautam; Yu, Keping
【Source】JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
【Abstract】Multi-agent systems enable the division of complicated tasks into individual objects that can cooperate. Such architecture can be useful in building solutions in the Internet of Medical Things (IoMT). In this paper, we propose an architecture of such a system that ensures the security of private data, as well as allows the addition and/or modification of the used classification methods. The main advantages of the proposed system are based on the implementation of blockchain technology elements and threaded federated learning. The individual elements are located on the agents who exchange information. Additionally, we propose building an agent with a consortium mechanism for classification results from many machine learning solutions. This proposal offers a new model of agents that can be implemented as a system for processing medical data in real-time. Our proposition was described and tested to present advantages over other, existing state-of-the-art methods. We show, that this proposition can improve the Internet of Medical Thing solutions by presenting a new idea of a multi-agent system that can separate different tasks like security, or classification and as a result minimize operation time and increase accuracy.
【Keywords】Access control; Federated learning; Blockchain; Internet of medical things; Image processing; Neural networks
【标题】基于联邦学习和区块链技术的智能医疗系统Agent架构
【摘要】多智能体系统可以将复杂的任务划分为可以合作的单个对象。这种架构可用于在医疗物联网 (IoMT) 中构建解决方案。在本文中，我们提出了这种系统的架构，以确保私有数据的安全性，并允许添加和/或修改使用的分类方法。所提出系统的主要优点是基于区块链技术元素和线程联邦学习的实施。各个元素位于交换信息的代理上。此外，我们建议构建一个具有联盟机制的代理，用于对许多机器学习解决方案的分类结果进行分类。该提案提供了一种新的代理模型，可以作为实时处理医疗数据的系统来实现。我们的提议被描述和测试，以呈现优于其他现有最先进方法的优势。我们表明，这个提议可以通过提出多代理系统的新理念来改进医疗物联网解决方案，该系统可以分离不同的任务，如安全或分类，从而最大限度地减少操作时间并提高准确性。
【关键词】访问控制;联邦学习；区块链；医疗物联网；图像处理;神经网络
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