QFSM: A Novel Quantum Federated Learning Algorithm for Speech Emotion Recognition With Minimal Gated Unit in 5G IoV

作     者：Qu, Zhiguo Chen, Zhixiao Dehdashti, Shahram Tiwari, Prayag 

作者机构：Jiangsu Collaborative Innovation Center of Atmospheric Environment the Equipment Technology and the Engineering Research Center of Digital Forensics Ministry of Education and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Emmy-Noether Gruppe Theoretisches Quantensystemdesign Lehrstuhl Für Theoretische Informationstechnik Technische Universität München Munich Germany School of Information Technology Halmstad University Halmstad Sweden 

出 版 物：《IEEE Transactions on Intelligent Vehicles》 (IEEE Trans. Intell. Veh.)

年 卷 期：2024年第9卷第10期

页      面：1-12页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 070206[理学-声学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China Innovation Program for Quantum Science and Technology PAPD and CICAEET funds Federal Ministry of Education and Research of Germany 

主　　题：Speech recognition 

摘      要：The technology of speech emotion recognition (SER) has been widely applied in the field of human-computer interaction within the Internet of Vehicles (IoV). The incorporation of emerging technologies such as artificial intelligence and big data has accelerated the advancement of SER technology. However, this reveals challenges such as limited computational resources, data processing inefficiency, and security and privacy concerns. In recent years, quantum machine learning has been applied to the field of intelligent transportation, which has demonstrated its various advantages, including high prediction accuracy, robust noise resistance, and strong security. This study first integrates quantum federated learning (QFL) into 5G IoV using a quantum minimal gated unit (QMGU) recurrent neural network for local training. Then, it proposes a novel quantum federated learning algorithm, QFSM, to further enhance computational efficiency and privacy protection. Experimental results demonstrate that compared to existing algorithms using quantum long short-term memory network or quantum gated recurrent unit models, the QFSM algorithm has a higher recognition accuracy and faster training convergence rate. It also performs better in terms of privacy protection and noise robustness, enhancing its applicability and practicality. IEEE