Abstract
Existing studies on relation extraction focus at the document level in a centralized training environment, requiring the collection of documents from various sources. However, this raises concerns about privacy protection, especially in sensitive domains such as finance and healthcare. For the first time, this work extends document-level relation extraction to a federated environment. The proposed federated framework, called FedLCC, is tailored for biomedical relation extraction that enables collaborative training without sharing raw medical texts. To fully exploit the models of all participating clients and improve the local training on individual clients, we propose a novel concept of localized context contrast on the basis of contrastive learning. By comparing and rectifying the similarity of localized context in documents between clients and the central server, the global model can better represent the documents on individual clients. Due to the lack of a widely accepted measure of non-IID text data, we introduce a novel non-IID scenario based on graph structural entropy. Experimental results on three document-level biomedical relation extraction datasets demonstrate the effectiveness of our method. Our code is available at https://github.com/xxxxyan/FedLCC.- Anthology ID:
- 2024.lrec-main.629
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 7163–7173
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.629
- DOI:
- Cite (ACL):
- Yan Xiao, Yaochu Jin, and Kuangrong Hao. 2024. Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7163–7173, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Federated Document-Level Biomedical Relation Extraction with Localized Context Contrast (Xiao et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.629.pdf