EAFL: Equilibrium Augmentation Mechanism to Enhance Federated Learning for Aspect Category Sentiment Analysis

Abstract

Aspect Category Sentiment Analysis (ACSA) involves identifying sentiment categories for specific aspects of a sentence. Despite the progress made in pre-trained language models for extracting semantic and categorical representations, the sharing of sensitive data remains challenging due to data privacy protection. Federated Learning (FL) offers a solution by enabling decentralized model training without accessing raw datasets. However, isolated data encounters difficulties with text heterogeneity, which is crucial for aspect-based sentiment analysis (ABSA) subtask like ACSA. To address these problems, this paper presents a novel method called the Equilibrium Augmentation Mechanism to Enhance Federated Learning for Aspect Category Sentiment Analysis (EAFL-ACSA). The EAFL mechanism seamlessly integrates FL’s data privacy preservation capabilities with a variety of augmented data representations. Our unified model safeguards data privacy through FL and incorporates multiple features through data augmentation to enhance encrypted data. Furthermore, the parameter-efficient tuning mechanism incorporated in our framework enhances model performance and generates fine-tuned augmented sentences. Extensive experiments on five benchmark datasets demonstrates that EAFL consistently outperforms the best models, achieving a competitive level of performance while protecting data privacy. The equilibrium data augmentation mechanism not only advances aspect category detection but also enhances semantic data representations, effectively addressing critical concerns surrounding data privacy preservation within a unified framework.