An Active Semi-Supervised Short Text Classification Method Based on Federated Learning

Short-text classification is broadly used and is a current hot research spot. However, the performance of short-text classification is hampered by the sca1rcity of annotated data for short texts and the challenges of centralized training for private data. To address these issues, we propose Fed-ASSL-HGAT (Active Semi-Supervised Heterogeneous Graph ATtention network model based on Federated learning), an active semi-supervised heterogeneous graph attention network model based on federated learning. This model utilizes the innovative active semi-supervised learning (ASSL) framework to generate high-quality labeled samples for empowering the heterogeneous graph attention network (HGAT) model. Additionally, federated learning is introduced to facilitate the joint training of the models deployed on different nodes, thereby satisfying the requirements of data privacy protection. The proposed ASSL framework significantly reduces the annotation difficulty by transforming the multi-class annotation task into a binary classification task. To mitigate information loss, we employ a selection strategy based on information gain to filter soft and hard labels. Semi-supervised learning is employed to select positive and negative samples with high accuracy and stability for pseudo-labeling, thereby ensuring the labeling quality. Experimental results demonstrate that the proposed ASSL-HGAT (Active Semi-supervised Learning Empowered Heterogeneous Graph Attention Network) model achieves improvements of 2.45%, 8.11%, and 7.46% in F1 scores comparing with the HGAT baseline model on the AGNews, Snippets, and TagMyNews datasets, respectively. By incorporating the federated learning, the Fed-ASSL-HGAT model can meet the performance requirements without scarifying data privacy. © 2024 Chinese Institute of Electronics. All rights reserved.