2024/11/18 by Duzhen Zhang, Zhang, Duzhen, Yahan Yu +7 · 3 citations
Computer Science · Decision Sciences · Engineering · #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Data Quality and Management #Engineering #Entity linking #FOS: Computer and information sciences #Information retrieval #Named-entity recognition #Natural language processing #Topic Modeling #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2411.11623
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Federated Named Entity Recognition (FNER) boosts model training within each local client by aggregating the model updates of decentralized local clients, without sharing their private data. However, existing FNER methods assume fixed entity types and local clients in advance, leading to their ineffectiveness in practical applications. In a more realistic scenario, local clients receive new entity types continuously, while new local clients collecting novel data may irregularly join the global FNER training. This challenging setup, referred to here as Federated Incremental NER, renders the global model suffering from heterogeneous forgetting of old entity types from both intra-client and inter-client perspectives. To overcome these challenges, we propose a Local-Global Forgetting Defense (LGFD) model. Specifically, to address intra-client forgetting, we develop a structural knowledge distillation loss to retain the latent space's feature structure and a pseudo-label-guided inter-type contrastive loss to enhance discriminative capability over different entity types, effectively preserving previously learned knowledge within local clients. To tackle inter-client forgetting, we propose a task switching monitor that can automatically identify new entity types under privacy protection and store the latest old global model for knowledge distillation and pseudo-labeling. Experiments demonstrate significant improvement of our LGFD model over comparison methods.