Multi-Site Clinical Federated Learning using Recursive and Attentive
  Models and NVFlare
Won Joon Yun Samuel Kim Joongheon Kim
The prodigious growth of digital health data has precipitated a mounting
interest in harnessing machine learning methodologies, such as natural language
processing (NLP), to scrutinize medical records, clinical notes, and other
text-based health information. Although NLP techniques have exhibited
substantial potential in augmenting patient care and informing clinical
decision-making, data privacy and adherence to regulations persist as critical
concerns. Federated learning (FL) emerges as a viable solution, empowering
multiple organizations to train machine learning models collaboratively without
disseminating raw data. This paper proffers a pragmatic approach to medical NLP
by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA.
We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based
model and Bidirectional Encoder Representations from Transformers (BERT), which
have demonstrated exceptional performance in comprehending context and
semantics within medical data. This paper encompasses the development of an
integrated framework that addresses data privacy and regulatory compliance
challenges while maintaining elevated accuracy and performance, incorporating
BERT pretraining, and comprehensively substantiating the efficacy of the
proposed approach.