  Building robust deep learning-based models requires diverse training data,
ideally from several sources. However, these datasets cannot be combined easily
because of patient privacy concerns or regulatory hurdles, especially if
medical data is involved. Federated learning (FL) is a way to train machine
learning models without the need for centralized datasets. Each FL client
trains on their local data while only sharing model parameters with a global
server that aggregates the parameters from all clients. At the same time, each
client's data can exhibit differences and inconsistencies due to the local
variation in the patient population, imaging equipment, and acquisition
protocols. Hence, the federated learned models should be able to adapt to the
local particularities of a client's data. In this work, we combine FL with an
AutoML technique based on local neural architecture search by training a
"supernet". Furthermore, we propose an adaptation scheme to allow for
personalized model architectures at each FL client's site. The proposed method
is evaluated on four different datasets from 3D prostate MRI and shown to
improve the local models' performance after adaptation through selecting an
optimal path through the AutoML supernet.
