  We propose a simple new aggregation strategy for federated learning that won
the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever
challenge on Federated Learning in the Machine Learning community. Our method
addresses the problem of how to aggregate multiple models that were trained on
different data sets. Conceptually, we propose a new way to choose the weights
when averaging the different models, thereby extending the current state of the
art (FedAvg). Empirical validation demonstrates that our approach reaches a
notable improvement in segmentation performance compared to FedAvg.
