联合影响者学习,在现实的医疗数据库环境中实现安全和高效的协作学习
Haengbok Chung1,2, Jae Sung Lee3,4,5
1Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Korea.
Scientific reports
|September 30, 2024
概括
联合影响者学习 (FIL) 通过在没有中央服务器的情况下实现安全协作来增强深度学习. 这种新的方法在医学成像任务中优于传统的联合学习.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗成像医学成像
背景情况:
- 深度学习需要大量的数据集,但集中式培训带来了数据所有权和安全风险.
- 传统的联合学习 (FL) 有局限性,包括对攻击的脆弱性,非IID数据,中央服务器依赖性和通信开销.
- FL并没有针对动态的医院数据库环境进行优化.
研究的目的:
- 引入联邦影响者学习 (FIL) 作为安全高效的协作学习模式.
- 解决FL在资源有限和隐私敏感环境 (如医院) 中的局限性.
- 开发一种适合动态数据积累的模型不可知训练方法.
主要方法:
- 与FL的服务器-客户端模型不同,FIL采用与管理员平等的地位结构.
- 该过程包括四个阶段:本地培训,资格 (影响者/追随者),选 (logit完整性) 和影响 (知识共享).
- FIL避免了模型参数交易和中央服务器,增强了安全性,并使模型不可知训练成为可能.
主要成果:
- 在医学 (X射线,MRI,PET) 和自然 (CIFAR-10) 图像数据集上,与几种FL方法相比,FIL表现优越.
- 实验是在动态积累的数据库环境中进行的,展示了FIL的适应性.
- 菲尔获得了更高的精度,回忆和子得分,参与者的标准偏差更低,特别是PET数据的子得分和回忆得到了40%的改善.
结论:
- 通过消除中央服务器和模型参数交换,FIL提供了比传统FL更高的安全性和效率.
- 模型不可知性和平等地位结构使FIL非常适合医疗保健和其他隐私关键领域.
- 文件有效地解决了在动态,分布式环境中协作学习的挑战.
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