  Federated learning (FL) enables collaborative model training while preserving
each participant's privacy, which is particularly beneficial to the medical
field. FedAvg is a standard algorithm that uses fixed weights, often
originating from the dataset sizes at each client, to aggregate the distributed
learned models on a server during the FL process. However, non-identical data
distribution across clients, known as the non-i.i.d problem in FL, could make
this assumption for setting fixed aggregation weights sub-optimal. In this
work, we design a new data-driven approach, namely Auto-FedAvg, where
aggregation weights are dynamically adjusted, depending on data distributions
across data silos and the current training progress of the models. We
disentangle the parameter set into two parts, local model parameters and global
aggregation parameters, and update them iteratively with a
communication-efficient algorithm. We first show the validity of our approach
by outperforming state-of-the-art FL methods for image recognition on a
heterogeneous data split of CIFAR-10. Furthermore, we demonstrate our
algorithm's effectiveness on two multi-institutional medical image analysis
tasks, i.e., COVID-19 lesion segmentation in chest CT and pancreas segmentation
in abdominal CT.
