Visualization for Federated Learning: Challenges and Framework

Federated learning(FL)is a distributed machine learning solution,which guarantees data privacy and security.Similar to the problem of the interpretability of traditional machine learning,how to explain FL is a new challenge.Based on the characteristics of the distribution and privacy security of FL methods,this paper explores how to design the visualization framework of FL.Traditional visualization tasks often require a large amount of data.However,the privacy feature of FL determines that it cannot retrieve clients’data.Therefore,the available data mainly comes from the training process of the server-side,including server-side model parameters and client training status.Based on the analysis of the challenge of the interpretability of FL,this paper designs a visualization framework that takes into account the clients,the server-side,and FL models.The framework consists of the classical FL model,data storage center,data processing module and visual analysis interface.Finally,this article introduces two existing visualization cases and discusses a more general visual analysis method in the future.