用联邦学习预测痴呆前兆，无需共享数据也能精准建模
Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
- 采用联邦学习框架，各机构本地训练模型不交换原始数据
- 模型性能与集中式机器学习相当，且各机构表现一致
- 相比单点模型，协同训练显著提升预测准确率
痴呆是一种渐进性认知功能衰退疾病，轻度认知障碍（MCI）常为其前期表现。以往研究多依赖传统机器学习方法预测MCI向痴呆转化，需共享敏感临床数据。本文提出基于联邦学习（FL）的隐私保护方案，在不共享数据的前提下，利用社会人口学与认知评估指标训练预测模型。我们模拟并比较了点对点（P2P）与客户端-服务器两种网络架构，实现协作学习。结果表明，联邦学习模型的预测性能与集中式机器学习相当，各临床机构在不共享本地数据的情况下表现相似；且其性能优于未协作的单点模型。本研究证明，联邦学习可在不牺牲模型效能的前提下，彻底消除数据共享需求。
原文摘要 · Abstract (English)
Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often serving as its precursor. The prediction of MCI to dementia conversion has been well studied, but previous studies have almost always focused on traditional Machine Learning (ML) based methods that require sharing sensitive clinical information to train predictive models. This study proposes a privacy-enhancing solution using Federated Learning (FL) to train predictive models for MCI to dementia conversion without sharing sensitive data, leveraging socio demographic and cognitive measures. We simulated and compared two network architectures, Peer to Peer (P2P) and client-server, to enable collaborative learning. Our results demonstrated that FL had comparable predictive performance to centralized ML, and each clinical site showed similar performance without sharing local data. Moreover, the predictive performance of FL models was superior to site specific models trained without collaboration. This work highlights that FL can eliminate the need for data sharing without compromising model efficacy.
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