Multi-Agent Visualization for Explaining Federated Learning

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.

Authors

• Xiguang Wei
• Quan Li
• Yang Liu College of Computer Science, Sichuan University Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education
• Han Yu Nanyang Technological University, Singapore
• Tianjian Chen
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China

Keywords

• AI: AI Modelling and Simulation
• AI: Human-Computer Interactive Systems
• AI: Multiagent Systems
• Applications: Education and training