Federated uncertainty-aware learning for distributed hospital ehr data
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 …
(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 …
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