  Federated learning is an emerging research paradigm for enabling
collaboratively training deep learning models without sharing patient data.
However, the data from different institutions are usually heterogeneous across
institutions, which may reduce the performance of models trained using
federated learning. In this study, we propose a novel heterogeneity-aware
federated learning method, SplitAVG, to overcome the performance drops from
data heterogeneity in federated learning. Unlike previous federated methods
that require complex heuristic training or hyper parameter tuning, our SplitAVG
leverages the simple network split and feature map concatenation strategies to
encourage the federated model training an unbiased estimator of the target data
distribution. We compare SplitAVG with seven state-of-the-art federated
learning methods, using centrally hosted training data as the baseline on a
suite of both synthetic and real-world federated datasets. We find that the
performance of models trained using all the comparison federated learning
methods degraded significantly with the increasing degrees of data
heterogeneity. In contrast, SplitAVG method achieves comparable results to the
baseline method under all heterogeneous settings, that it achieves 96.2% of the
accuracy and 110.4% of the mean absolute error obtained by the baseline in a
diabetic retinopathy binary classification dataset and a bone age prediction
dataset, respectively, on highly heterogeneous data partitions. We conclude
that SplitAVG method can effectively overcome the performance drops from
variability in data distributions across institutions. Experimental results
also show that SplitAVG can be adapted to different base networks and
generalized to various types of medical imaging tasks.
