联邦持续学习用于隐私保护的医院影像分类
Federated Continual Learning for Privacy-Preserving Hospital Imaging Classification
AI总结：
本文提出DP-FedEPC方法，结合EWC、原型回放和差分隐私，解决医院影像分类中的隐私保护与持续学习问题。
摘要：
深度学习模型在放射学解释中越来越多地依赖多机构数据，但隐私法规和医院间的分布偏移限制了中央数据池的建立。联邦学习（FL）允许医院在不共享原始图像的情况下协同训练模型，但当前的FL算法通常假设数据分布是静态的。实际上，医院在病例构成、标注协议和成像设备方面经历持续的变化，这导致在模型顺序更新时出现灾难性遗忘。联邦持续学习（FCL）旨在解决这些挑战，但现有方法要么忽视了医疗的严格隐私约束，要么依赖于回放缓冲区和公开的替代数据集，这些在临床环境中难以合理化。我们研究了在医院作为客户端接收随时间变化的病例和标签的设定下的FCL，用于胸片分类。我们引入了DP-FedEPC（差分隐私联邦弹性原型凝聚），一种结合弹性权重凝聚（EWC）、基于原型的回放和客户端侧差分隐私的标准化FedAvg框架。EWC限制更新沿被认为对先前任务重要的参数，而一个保存潜在原型的内存保留类别结构而不存储原始图像。每个客户端上的差分隐私随机梯度下降（DP-SGD）在裁剪后的梯度上添加校准的高斯噪声，为单个放射图像提供正式的隐私保证。
英文摘要：
Deep learning models for radiology interpretation increasingly rely on multi-institutional data, yet privacy regulations and distribution shift across hospitals limit central data pooling. Federated learning (FL) allows hospitals to collaboratively train models without sharing raw images, but current FL algorithms typically assume a static data distribution. In practice, hospitals experience continual evolution in case mix, annotation protocols, and imaging devices, which leads to catastrophic forgetting when models are updated sequentially. Federated continual learning (FCL) aims to reconcile these challenges but existing methods either ignore the stringent privacy constraints of healthcare or rely on replay buffers and public surrogate datasets that are difficult to justify in clinical settings. We study FCL for chest radiography classification in a setting where hospitals are clients that receive temporally evolving streams of cases and labels. We introduce DP-FedEPC (Differentially Private Federated Elastic Prototype Consolidation), a method that combines elastic weight consolidation (EWC), prototype-based rehearsal, and client-side differential privacy within a standard FedAvg framework. EWC constrains updates along parameters deemed important for previous tasks, while a memory of latent prototypes preserves class structure without storing raw images. Differentially private stochastic gradient descent (DP-SGD) at each client adds calibrated Gaussian noise to clipped gradients, providing formal privacy guarantees for individual radiographs.
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