  We evaluate the performance of federated learning (FL) in developing deep
learning models for analysis of digitized tissue sections. A classification
application was considered as the example use case, on quantifiying the
distribution of tumor infiltrating lymphocytes within whole slide images
(WSIs). A deep learning classification model was trained using 50*50 square
micron patches extracted from the WSIs. We simulated a FL environment in which
a dataset, generated from WSIs of cancer from numerous anatomical sites
available by The Cancer Genome Atlas repository, is partitioned in 8 different
nodes. Our results show that the model trained with the federated training
approach achieves similar performance, both quantitatively and qualitatively,
to that of a model trained with all the training data pooled at a centralized
location. Our study shows that FL has tremendous potential for enabling
development of more robust and accurate models for histopathology image
analysis without having to collect large and diverse training data at a single
location.
