  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.
