Agent architecture of an intelligent medical system based on federated learning and blockchain technology

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.