通过服务器旋转的联合机器学习来实现跨供应商联合诊断成像的协作和保护隐私
Hao Wang1, Xiaoyu Zhang1, Xuebin Ren2
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
Communications engineering
|August 9, 2025
概括
联合学习 (FL) 得到了新的服务器旋转方法 (SRFLM) 的增强,该方法解决了医疗成像中的数据异质性和固定服务器限制. 这种方法可以改善跨供应商的协作模式培训,同时确保数据隐私.
科学领域:
- 医疗成像医学成像
- 分布式系统 分布式系统
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 允许跨多个数据供应商进行协作模式培训,同时保持隐私.
- 传统的FL与数据异质性和固定的服务器限制作斗争,影响诊断成像性能.
- 现有的方法对于动态的,多供应商的医疗数据环境缺乏灵活性.
研究的目的:
- 引入一种新的服务器旋转联合学习方法 (SRFLM),以克服跨供应商医疗成像方面的挑战.
- 提高FL在异质医疗数据集中的灵活性和性能.
- 在协作模式培训期间确保强大的数据隐私.
主要方法:
- 拟议的SRFLM将供应商指定为临时服务器,轮流进行联合微调.
- 实施了旋转服务器通信机制和动态服务器选择策略.
- 将差异隐私集成到通信协议中,以提高数据安全性.
主要成果:
- 在跨供应商的诊断成像任务中,SRFLM表现得更好.
- 旋转服务器方法有效地处理了供应商之间的数据异质性.
- 动态服务器选择确保了持续和灵活的协作培训.
结论:
- SRFLM提供了一种灵活且保护隐私的解决方案,用于协作医疗成像模型培训.
- 该方法解决了传统FL在现实世界多供应商场景中的关键局限性.
- SRFLM在各种医疗数据源中促进了统一的诊断成像能力.
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