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Large scale contextual representation models, such as BERT, havesignificantly advanced natural language processing (NLP) in recently years.However, in certain area like healthcare, accessing diverse large scale textdata from multiple institutions is extremely challenging due to privacy andregulatory reasons. In this article, we show that it is possible to bothpretrain and fine tune BERT models in a federated manner using clinical textsfrom different silos without moving the data.

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos