Multi-Agent Visualization for Explaining Federated Learning

Xiguang Wei, Quan Li, Yang Liu, Han Yu, Tianjian Chen, Qiang Yang

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Demos. Pages 6572-6574.

https://doi.org/10.24963/ijcai.2019/960

PDF BibTeX

Abstract

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.

Keywords

  • Applications: Education and training
  • AI: Human-Computer Interactive Systems
  • AI: Multiagent Systems
  • AI: AI Modelling and Simulation
Welcome Future Conferences AI Journal Division IJCAI Proceedings Travel Grant Program IJCAI Awards