Abstract
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing all clients 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 data from various scanners and patient demographics. This problem becomes even more significant when deploying the global model to unseen clients outside the FL with new distributions not presented during federated training. To optimize the prediction accuracy of each individual client for critical medical tasks, we propose a novel unified framework for both Inside and Outside model Personalization in FL (IOP-FL). Our inside personalization is achieved by a lightweight gradient-based approach that exploits the local adapted model for each client, by accumulating both the global gradients for common knowledge and 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 a new model for outside FL clients. Hence, we design a new test-time routing scheme inspired by 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 great potential of our IOP-FL scheme for clinical practice. Code will be released at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2204.08467
