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

Dawid Połap, Gautam Srivastava*, Keping Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Article number102748
JournalJournal of Information Security and Applications
Volume58
DOIs
Publication statusPublished - 2021 May

Keywords

  • Access control
  • Blockchain
  • Federated learning
  • Image processing
  • Internet of medical things
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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