  Federated learning (FL) has been a promising approach in the field of medical
imaging in recent years. A critical problem in FL, specifically in medical
scenarios is to have a more accurate shared model which is robust to noisy and
out-of distribution clients. In this work, we tackle the problem of statistical
heterogeneity in data for FL which is highly plausible in medical data where
for example the data comes from different sites with different scanner
settings. We propose IDA (Inverse Distance Aggregation), a novel adaptive
weighting approach for clients based on meta-information which handles
unbalanced and non-iid data. We extensively analyze and evaluate our method
against the well-known FL approach, Federated Averaging as a baseline.
