CDHFL-HA: Collaborative Dynamic Hierarchical Federated Learning With Hypernetwork Aggregation for Sentimental Analysis

In recent years, more and more scholars have begun to focus on sentiment analysis on social media. Current sentiment analysis collects all relevant data, including public thoughts, opinions, and feelings, from a variety of open sources. In addition, it automatically predicts different aspects of outcomes or trends based on information collected globally in real time. This research area explores how to extract sentiment information from different modalities (e.g., text, images, and audio). However, the currently existing techniques face several challenges. It is difficult to achieve effective interaction with completely heterogeneous data, and these techniques cannot adequately guarantee data security during data interaction, which is particularly important when dealing with sensitive information. Therefore, this article introduces existing methods for protecting data privacy. Based on this foundation, we propose a novel algorithm called collaborative dynamic hierarchical federated learning with hypernetwork aggregation (CDHFL-HA), which is suitable for sentimental analysis. CDHFL-HA ensures that the data remain local to each participant while leveraging the data similarity between participants on the server and processing interference data on the participant to enhance the accuracy of the current sentimental analysis. In addition, an essential aspect considered in the proposed algorithm is explainability. Understanding the decisions and predictions made by sentiment analysis models is crucial for gaining trust and acceptance in real-world applications. CDHFL-HA incorporates explainability features, providing insights into the decision-making process, thus enhancing the interpretability of sentiment analysis results. Numerous experimental results show that the algorithm outperforms existing algorithms in complex scenarios, with a minimum accuracy of 0.6007 and a maximum of 0.9962. In addition, it can be seen from the experimental results in this article, that the communication parameters in the experiments are similar to those of other federated learning, while the number of training rounds is improved by up to 50% (i.e., 20 rounds faster) relative to other algorithms.