# FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning

arXiv: https://arxiv.org/abs/2003.09288 · HTML: https://mlsanity.com/paper/2003.09288/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2020.

## Built on by (most cited first, 24 of 24)

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- [Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse](https://mlsanity.com/paper/2303.13451/) (2023)
- [Unintended Memorization and Timing Attacks in Named Entity Recognition Models](https://mlsanity.com/paper/2211.02245/) (2022)
- [Federated Incremental Named Entity Recognition](https://mlsanity.com/paper/2411.11623/) (2024)
- [Bangla MedER: Multi-BERT Ensemble Approach for the Recognition of Bangla Medical Entity](https://mlsanity.com/paper/2512.17769/) (2025)
- [Federated Named Entity Recognition](https://mlsanity.com/paper/2203.15101/) (2022)
- [A Federated Approach for Hate Speech Detection](https://mlsanity.com/paper/2302.09243/) (2023)
- [Adaptive Federated Distillation for Multi-Domain Non-IID Textual Data](https://mlsanity.com/paper/2508.20557/) (2025)
- [Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data](https://mlsanity.com/paper/2312.04281/) (2023)

## Abstract (arXiv)

Medical named entity recognition (NER) has wide applications in intelligent healthcare. Sufficient labeled data is critical for training accurate medical NER model. However, the labeled data in a single medical platform is usually limited. Although labeled datasets may exist in many different medical platforms, they cannot be directly shared since medical data is highly privacy-sensitive. In this paper, we propose a privacy-preserving medical NER method based on federated learning, which can leverage the labeled data in different platforms to boost the training of medical NER model and remove the need of exchanging raw data among different platforms. Since the labeled data in different platforms usually has some differences in entity type and annotation criteria, instead of constraining different platforms to share the same model, we decompose the medical NER model in each platform into a shared module and a private module. The private module is used to capture the characteristics of the local data in each platform, and is updated using local labeled data. The shared module is learned across different medical platform to capture the shared NER knowledge. Its local gradients from different platforms are aggregated to update the global shared module, which is further delivered to each platform to update their local shared modules. Experiments on three publicly available datasets validate the effectiveness of our method.