  Centralized training methods have shown promising results in MR image
reconstruction, but privacy concerns arise when gathering data from multiple
institutions. Federated learning, a distributed collaborative training scheme,
can utilize multi-center data without the need to transfer data between
institutions. However, existing federated learning MR image reconstruction
methods rely on manually designed models which have extensive parameters and
suffer from performance degradation when facing heterogeneous data
distributions. To this end, this paper proposes a novel FederAted neUral
archiTecture search approach fOr MR Image reconstruction (FedAutoMRI). The
proposed method utilizes differentiable architecture search to automatically
find the optimal network architecture. In addition, an exponential moving
average method is introduced to improve the robustness of the client model to
address the data heterogeneity issue. To the best of our knowledge, this is the
first work to use federated neural architecture search for MR image
reconstruction. Experimental results demonstrate that our proposed FedAutoMRI
can achieve promising performances while utilizing a lightweight model with
only a small number of model parameters compared to the classical federated
learning methods.
