Collaborative and privacy-preserving cross-vendor united diagnostic imaging via server-rotating federated machine learning.
Authors
Affiliations (8)
Affiliations (8)
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
- National Engineering Laboratory for Big Data Analytics, Xi'an Jiaotong University, Xi'an, China.
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
- Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, China.
- Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, China. [email protected].
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China. [email protected].
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China. [email protected].
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China. [email protected].
Abstract
Federated Learning (FL) is a distributed framework that enables collaborative training of a server model across medical data vendors while preserving data privacy. However, conventional FL faces two key challenges: substantial data heterogeneity among vendors and limited flexibility from a fixed server, leading to suboptimal performance in diagnostic-imaging tasks. To address these, we propose a server-rotating federated learning method (SRFLM). Unlike traditional FL, SRFLM designates one vendor as a provisional server for federated fine-tuning, with others acting as clients. It uses a rotational server-communication mechanism and a dynamic server-election strategy, allowing each vendor to sequentially assume the server role over time. Additionally, the communication protocol of SRFLM provides strong privacy guarantees using differential privacy. We extensively evaluate SRFLM across multiple cross-vendor diagnostic imaging tasks. We envision SRFLM as paving the way to facilitate collaborative model training across medical data vendors, thereby achieving the goal of cross-vendor united diagnostic imaging.