PAFedMIS: Personalized Asynchronous Federated Learning for Medical Image Segmentation

Yi Li, Yue Hua, Xin Zheng, Yanqing Guo, Bo Wang

Abstract: As privacy protection gains momentum, federated learning has emerged as a cutting-edge approach in medical image analysis. However, the intricacies of medical image segmentation task have led to a dearth of research in this domain, with existing studies falling short in tackling two pivotal challenges: The traditional model with the uniform global model underperforms for certain clients due to the heterogeneity and non-Independent Identically Distributed(non-IID) data across medical institutions. And the communication between the server and clients often incurs significant time costs. This paper introduces a novel Personalized Asynchronous Federated learning for Medical Image Segmentation model, dubbed PAFedMIS, to mitigate the negative impact of the heterogeneous data and fully utilized the waiting time, in medical image segmentation. Comprehensive experiments on ISIC2018 demonstrate the enhanced model accuracy and training efficiency of PAFedMIS.

External IDs:dblp:conf/icassp/0018HZG025