  Achieving domain generalization in medical imaging poses a significant
challenge, primarily due to the limited availability of publicly labeled
datasets in this domain. This limitation arises from concerns related to data
privacy and the necessity for medical expertise to accurately label the data.
In this paper, we propose a federated learning approach to transfer knowledge
from multiple local models to a global model, eliminating the need for direct
access to the local datasets used to train each model. The primary objective is
to train a global model capable of performing a wide variety of medical imaging
tasks. This is done while ensuring the confidentiality of the private datasets
utilized during the training of these models. To validate the effectiveness of
our approach, extensive experiments were conducted on eight datasets, each
corresponding to a different medical imaging application. The client's data
distribution in our experiments varies significantly as they originate from
diverse domains. Despite this variation, we demonstrate a statistically
significant improvement over a state-of-the-art baseline utilizing masked image
modeling over a diverse pre-training dataset that spans different body parts
and scanning types. This improvement is achieved by curating information
learned from clients without accessing any labeled dataset on the server.
