Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
英文摘要：
Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years. However, in certain area like healthcare, accessing diverse large scale text data from multiple institutions is extremely challenging due to privacy and regulatory reasons. In this article, we show that it is possible to both pretrain and fine tune BERT models in a federated manner using clinical texts from different silos without moving the data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
