Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous studies suggest that domain shift across different sites may influence the performance of federated models. As a solution, we propose a gradient matching federated domain adaptation (GM-FedDA) method for brain image classification, aiming to reduce domain discrepancy with the assistance of a public image dataset and train robust local federated models for target sites. It mainly includes two stages: 1) pretraining stage; we propose a one-common-source adversarial domain adaptation (OCS-ADA) strategy, i.e., adopting ADA with gradient matching loss to pretrain encoders for reducing domain shift at each target site (private data) with the assistance of a common source domain (public data) and 2) fine-tuning stage; we develop a gradient matching federated (GM-Fed) fine-tuning method for updating local federated models pretrained with the OCS-ADA strategy, i.e., pushing the optimization direction of a local federated model toward its specific local minimum by minimizing gradient matching loss between sites. Using fully connected networks as local models, we validate our method with the diagnostic classification tasks of schizophrenia and major depressive disorder based on multisite resting-state functional MRI (fMRI), respectively. Results show that the proposed GM-FedDA method outperforms other commonly used methods, suggesting the potential of our method in brain imaging analysis and other fields, which need to utilize multisite data while preserving data privacy.

中文翻译：

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用于脑图像分类的梯度匹配联合域适应


联邦学习在许多不同的任务中展现了其独特的优势，包括大脑图像分析。它提供了一种训练深度学习模型的新方法，同时保护来自多个站点的医学图像数据的隐私。然而，之前的研究表明，不同站点之间的域转移可能会影响联合模型的性能。作为解决方案，我们提出了一种用于脑图像分类的梯度匹配联合域适应（GM-FedDA）方法，旨在借助公共图像数据集减少域差异，并为目标站点训练鲁棒的本地联合模型。主要包括两个阶段：1）预训练阶段；我们提出了一种单公共源对抗域适应（OCS-ADA）策略，即采用具有梯度匹配损失的 ADA 来预训练编码器，以在公共源域的帮助下减少每个目标站点（私有数据）的域移位（公开数据）和2）微调阶段；我们开发了一种梯度匹配联邦（GM-Fed）微调方法，用于更新使用 OCS-ADA 策略预训练的局部联邦模型，即通过最小化之间的梯度匹配损失，将局部联邦模型的优化方向推向其特定的局部最小值。网站。使用完全连接的网络作为局部模型，我们分别通过基于多站点静息态功能 MRI (fMRI) 的精神分裂症和重度抑郁症的诊断分类任务来验证我们的方法。结果表明，所提出的 GM-FedDA 方法优于其他常用方法，表明我们的方法在脑成像分析和其他需要利用多站点数据同时保护数据隐私的领域中具有潜力。