FedCE: A Contrast Enhancement Federated Learning Method for Heterogeneous Medical Named Entity Recognition

Medical Named Entity Recognition (NER) plays a crucial role in attaining precise patient portraits as well as providing support for intelligent diagnosis and treatment decisions. Federated Learning (FL) enables collaborative modeling and training across multiple endpoints without exposing the original data. However, the statistical heterogeneity exhibited by clinical medical text records poses a challenge for FL methods to support the training of NER models in such scenarios. We propose a Federated Contrast Enhancement (FedCE) method for NER to address the challenges faced by non-large-scale pre-trained models in FL for label-heterogeneous. The method leverages a multi-view encoder structure to capture both global and local semantic information, and employs contrastive learning to enhance the interoperability of global knowledge and local context. We evaluate the performance of the FedCE method on three real-world clinical record datasets. We investigate the impact of factors, such as pooling methods, maximum input text length, and training rounds on FedCE. Additionally, we assess how well FedCE adapts to the base NER models and evaluate its generalization performance. The experimental results show that the FedCE method has obvious advantages and can be effectively applied to various basic models, which is of great theoretical and practical significance for advancing FL in healthcare settings.