Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data. It is difficult, if possible at all, for such a global model to commonly achieve optimal performance for each individual client, due to the heterogeneity of medical images from various scanners and patient demographics. This problem becomes even more significant when deploying the global model to unseen clients outside the FL with unseen distributions not presented during federated training. To optimize the prediction accuracy of each individual client for medical imaging tasks, we propose a novel unified framework for both \textit{Inside and Outside model Personalization in FL} (IOP-FL). Our inside personalization uses a lightweight gradient-based approach that exploits the local adapted model for each client, by accumulating both the global gradients for common knowledge and the local gradients for client-specific optimization. Moreover, and importantly, the obtained local personalized models and the global model can form a diverse and informative routing space to personalize an adapted model for outside FL clients. Hence, we design a new test-time routing scheme using the consistency loss with a shape constraint to dynamically incorporate the models, given the distribution information conveyed by the test data. Our extensive experimental results on two medical image segmentation tasks present significant improvements over SOTA methods on both inside and outside personalization, demonstrating the potential of our IOP-FL scheme for clinical practice.

翻译：联邦学习（FL）允许多个医疗机构在不集中客户端数据的情况下协同训练全局模型。由于来自不同扫描仪和患者群体的医学图像存在异质性，全局模型即便可能，也难以针对每个客户端实现最优性能。当将全局模型部署到联邦学习外部、其数据分布未在联邦训练中出现过的未见客户端时，该问题更为突出。为优化医学成像任务中每个客户端个体的预测精度，我们提出了一种新颖的统一框架，即《联邦学习中的内外模型个性化》（IOP-FL）。我们的内部个性化采用轻量级梯度方法，通过累积共同知识的全局梯度和客户端特定优化的局部梯度，利用每个客户端的局部自适应模型。此外，更重要的是，所获得的局部个性化模型与全局模型可构建一个多样且信息丰富的路由空间，从而为联邦学习外部的客户端定制适配模型。因此，我们设计了一种新的测试时路由方案，利用带形状约束的一致性损失，根据测试数据呈现的分布信息动态整合模型。在两个医学图像分割任务上的大量实验结果表明，我们的方法在内部和外部个性化方面均显著优于现有最佳方法，证明了IOP-FL方案在临床实践中的潜力。