  Federated learning (FL) enables collaborative learning of a deep learning
model without sharing the data of participating sites. FL in medical image
analysis tasks is relatively new and open for enhancements. In this study, we
propose FedDropoutAvg, a new federated learning approach for training a
generalizable model. The proposed method takes advantage of randomness, both in
client selection and also in federated averaging process. We compare
FedDropoutAvg to several algorithms in an FL scenario for real-world multi-site
histopathology image classification task. We show that with FedDropoutAvg, the
final model can achieve performance better than other FL approaches and closer
to a classical deep learning model that requires all data to be shared for
centralized training. We test the trained models on a large dataset consisting
of 1.2 million image tiles from 21 different centers. To evaluate the
generalization ability of the proposed approach, we use held-out test sets from
centers whose data was used in the FL and for unseen data from other
independent centers whose data was not used in the federated training. We show
that the proposed approach is more generalizable than other state-of-the-art
federated training approaches. To the best of our knowledge, ours is the first
study to use a randomized client and local model parameter selection procedure
in a federated setting for a medical image analysis task.
