  Recent works have shown that applying Machine Learning to Electronic Health
Records (EHR) can strongly accelerate precision medicine. This requires
developing models based on diverse EHR sources. Federated Learning (FL) has
enabled predictive modeling using distributed training which lifted the need of
sharing data and compromising privacy. Since models are distributed in FL, it
is attractive to devise ensembles of Deep Neural Networks that also assess
model uncertainty. We propose a new FL model called Federated Uncertainty-Aware
Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the
context of EHR. FUALA embeds uncertainty information in two ways: It reduces
the contribution of models with high uncertainty in the aggregated model. It
also introduces model ensembling at prediction time by keeping the last layers
of each hospital from the final round. In FUALA, the Federator (central node)
sends at each round the average model to all hospitals as well as a randomly
assigned hospital model update to estimate its generalization on that hospital
own data. Each hospital sends back its model update as well a generalization
estimation of the assigned model. At prediction time, the model outputs C
predictions for each sample where C is the number of hospital models. The
experimental analysis conducted on a cohort of 87K deliveries for the task of
preterm-birth prediction showed that the proposed approach outperforms FedAvg
when evaluated on out-of-distribution data. We illustrated how uncertainty
could be measured using the proposed approach.
