基于迭代协作学习框架的信誉医学参与方选择
Selection of Reputable Medical Participants Based on an Iterative Collaborative Learning Framework
陆枫 1李炜 2顾琳 1刘帅 1王润衡 1任宇飞 3戴小海 1廖小飞 1金海1
作者信息
- 1. 大数据技术与系统国家地方联合工程研究中心(华中科技大学) 武汉 430074;服务计算技术与系统教育部重点实验室(华中科技大学) 武汉 430074;集群与网格计算湖北省重点实验室(华中科技大学) 武汉 430074;华中科技大学计算机科学与技术学院 武汉 430074
- 2. 中澳能源信息学和需求响应技术联合研究中心(悉尼大学) 悉尼2006;分布式与高性能计算中心(悉尼大学) 悉尼2006;悉尼大学计算机学院 悉尼2006
- 3. 华中科技大学同济医学院附属同济医院计算机中心 武汉 430030
- 折叠
摘要
联邦学习和群智学习等协作学习技术,能够在保护数据隐私的条件下充分利用分布在各地的分布式数据深度挖掘数据中所蕴含的知识,拥有非常广阔的应用前景,尤其是在强调隐私惯例和道德约束的医疗健康领域.任何协作工作都需要选择可靠的参与方,协作学习中全局模型的性能在很大程度上取决于参与方的选择.然而,现有研究在选择参与方时都没有对不同机构医疗数据中存在的异质性加以直接关注.导致包含稳定性在内的全局模型的性能难以得到保障.提出了从信誉的角度尝试探索求解该问题.以迭代协作学习的方式尽可能挑选出具有良好信誉的参与方进行协作学习,以获得稳定可靠的高性能全局模型.首先,提出了一个描述医疗机构数据质量的AI信誉值指标AMP(AI medical promise),以帮助其在医疗领域中形成良好的AI生态.其次,建立了一个基于后向选择的迭代协作学习(colback-learning)框架.在单次协作学习任务中,利用后向选择方法以多项式时间复杂度迭代计算出性能良好且稳定的全局模型,完成AMP计算和积累.在AMP信誉值计算中,制定了一个综合考虑全局性能指标的评分函数,以针对医疗领域更有效地指导全局模型的训练.最后,使用真实医疗数据模拟多样化的协作学习场景.实验表明,colback-learning能够选择可靠参与方训练得到性能良好的全局模型,模型的性能稳定性比现有最好的参与方选择方法提高了1.3～6倍.全局模型的可解释性与集中式学习保持了较高的一致性.
Abstract
Collaborative learning technologies such as federated learning and swarming learning can fully use distributed data to deeply mine the knowledge contained in the data while protecting data privacy.It has a broad application prospect,especially in the medical and health field,where privacy practices and ethical constraints are emphasized.Collaborative efforts always require reliable participants.The performance of the global model in collaborative learning largely depends on participant selection.However,the existing studies need to pay more attention to the heterogeneity of medical participants'data.As a result,the performance of the global model,including stability,is difficult to be guaranteed.We propose to solve this problem from the perspective of reputation.Through iterative collaborative learning,reputation participants are selected as much as possible to obtain a stable and reliable high-performance global model in collaborative learning.We first propose an AI medical promise(AMP)to describe a medical institution's data quality and help form a good AI ecosystem in the medical field.Secondly,an iterative collaborative learning framework based on backward selection(colback-learning)is established.The backward selection method is used to iteratively calculate a well-performing and stable global model in polynomial time complexity to complete AMP calculation and accumulation in a single collaborative learning task.In calculating AMP,a scoring function that comprehensively considers global performance indicators is formulated to guide the training of the global model in the medical field.Finally,using real-world medical data to simulate various collaborative learning scenarios,we have shown that the colback-learning can select reliable participants to obtain a global model with good performance.The model's performance stability is 1.3 to 6 times higher than that of the state-of-the-art methods.The interpretability of the global model maintains a high consistency with centralized learning.
关键词
协作学习/联邦学习/参与方选择/数据贡献/区块链/神经网络Key words
collaborative learning/federated learning/participant selection/data contribution/block chain/neural networks引用本文复制引用
基金项目
国家自然科学基金重点项目(62232012)
湖北医疗健康大数据分析平台与智能服务项目()
出版年
2024
NETL
NSTL
万方数据