A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacy-sensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security requirements. We decompose the named entity recognition (NER) model FLAT (Chinese NER Using Flat-Lattice Transformer) in each platform into a global part and a local part. The local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled data. The global part is learned across different operation platforms to capture the shared NER knowledge. Its local gradients from different platforms are aggregated to update the global model, which is further delivered to each platform to update their global part. Experiments on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.