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The prodigious growth of digital health data has precipitated a mountinginterest in harnessing machine learning methodologies, such as natural languageprocessing (NLP), to scrutinize medical records, clinical notes, and othertext-based health information. Although NLP techniques have exhibitedsubstantial potential in augmenting patient care and informing clinicaldecision-making, data privacy and adherence to regulations persist as criticalconcerns. Federated learning (FL) emerges as a viable solution, empoweringmultiple organizations to train machine learning models collaboratively withoutdisseminating raw data. This paper proffers a pragmatic approach to medical NLPby amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA.We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-basedmodel and Bidirectional Encoder Representations from Transformers (BERT), whichhave demonstrated exceptional performance in comprehending context andsemantics within medical data. This paper encompasses the development of anintegrated framework that addresses data privacy and regulatory compliancechallenges while maintaining elevated accuracy and performance, incorporatingBERT pretraining, and comprehensively substantiating the efficacy of theproposed approach.

Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare