Prototypical clustered federated learning for heart rate prediction
Regular Papers|Updated:2025-11-18
|
Prototypical clustered federated learning for heart rate prediction
Enhanced Publication
面向心率预测的原型聚类联邦学习
“In the realm of heart rate prediction, a novel framework named PCFedH has been introduced, leveraging personalized federated learning and prototypical contrastive learning to enhance privacy protection and address data heterogeneity. This approach, which includes a federated clustering module and a soft clustered federated learning module, has been proven to significantly outperform existing methods, with a notable 3.1% reduction in mean squared error across datasets. The personalization component stands out as a key contributor to the framework's success.”
Frontiers of Information Technology & Electronic EngineeringVol. 26, Issue 10, Pages: 1896-1912(2025)
Affiliations:
1.School of Computer Science, Fudan University, Shanghai 200438, China
2.NIO, Shanghai 201805, China
3.Information System Department, University of Cologne, Cologne 50969, Germany
4.Department of Agricultural Sciences, University of Helsinki, Helsinki 00014, Finland
5.School of Computing, Macquarie University, Sydney 2109, Australia
6.Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China