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

IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS
Zhiguo Qu;Jian Ding;Bo Liu;Le Sun;Shahid Mumtaz
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引用次数: 0

Abstract

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.
基于超网络聚合的情感分析协同动态分层联邦学习
近年来,越来越多的学者开始关注社交媒体上的情感分析。当前情绪分析收集所有相关数据,包括公众的想法、意见和感受,来自各种开放来源。此外,它还可以根据全球实时收集的信息自动预测结果或趋势的不同方面。该研究领域探索如何从不同的模式(例如,文本,图像和音频)中提取情感信息。然而,现有的技术面临着一些挑战。与完全异构的数据很难实现有效的交互,并且这些技术不能充分保证数据交互过程中的数据安全,这在处理敏感信息时尤为重要。因此,本文介绍了现有的数据隐私保护方法。在此基础上,提出了一种适用于情感分析的基于超网络聚合的协同动态分层联邦学习算法(CDHFL-HA)。CDHFL-HA确保数据保持在每个参与者的本地,同时利用服务器上参与者之间的数据相似性,并处理参与者的干扰数据,以提高当前情感分析的准确性。此外,该算法考虑的一个重要方面是可解释性。理解情感分析模型做出的决策和预测对于在现实应用中获得信任和接受至关重要。CDHFL-HA纳入了可解释性特征,提供了对决策过程的洞察,从而增强了情感分析结果的可解释性。大量实验结果表明,该算法在复杂场景下优于现有算法,最小精度为0.6007,最大精度为0.9962。此外,从本文的实验结果可以看出,实验中的通信参数与其他联邦学习相似,而训练轮数相对于其他算法提高了高达50%(即快了20轮)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Computational Social Systems
IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)
CiteScore
10.00
自引率
20.00%
发文量
316
期刊介绍: IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.
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