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- Title
An in-depth evaluation of federated learning on biomedical natural language processing for information extraction.
- Authors
Peng, Le; Luo, Gaoxiang; Zhou, Sicheng; Chen, Jiandong; Xu, Ziyue; Sun, Ju; Zhang, Rui
- Abstract
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: (1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data; (2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. (3) FL models significantly outperformed pre-trained LLMs with few-shot prompting.
- Subjects
STATISTICAL models; DATA security; PSYCHOLOGICAL resilience; INTERPROFESSIONAL relations; MEDICAL informatics; DATABASE management; DATA mining; PRIVACY; NATURAL language processing; BIOINFORMATICS; INFORMATION retrieval; MACHINE learning; LEARNING strategies; MEDICAL ethics; ALGORITHMS
- Publication
NPJ Digital Medicine, 2024, Vol 7, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-024-01126-4