Mean Field Guided Machine Learning

Dr, Yuhan Kang  received the B.S. degree from the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China, in 2019, and the Ph.D. degree with the Electrical and Computer Engineering Department, the University of Houston, Houston, TX, USA in 2023. Currently he is AI Research Scientist for Weichai America Corp. His research interest include mean-field game theory, machine learning, deep learning, Internet-of-Things networks, and optimization theory.Dr. Hao Gao received the B.E. degree in electrical and information engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2018, and Ph.D. degree in electrical engineering with the University of Houston, Houston, TX, USA in 2022. Currently he is senior Engineering in Samsung, California, USA. His research interests include mean field game, machine learning, and related applications in wireless communication.Dr. Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as in the Computer Science Department at the University of Houston, Texas. Dr. Han received IEEE fellow since 2014, AAAS fellow since 2019, and ACM Fellow since 2024. Dr. Han is a 1% highly cited researcher since 2017 according to Web of Science. Dr. Han is also the winner of the 2021 IEEE Kiyo Tomiyasu Award (an IEEE Field Award), for outstanding early to mid-career contributions to technologies holding the promise of innovative applications, with the following citation: for contributions to game theory and distributed management of autonomous communication networks.

• Preface.- Chapter 1 Overview of Mean Field Theory and Machine Learning.- Chapter 2 Mean Field Game and Machine Learning Basis.- Chapter 3 Opinion Evolution in Social Networks: Use Generative Adversarial Networks to Solve Mean Field Game.- Chapter 4 Data Augmentation using Mean Field Games.- Chapter 5 Mean Field Game Guided Deep Reinforcement Learning.- Chapter 6 Incentive Mechanism Design in Satellite-Based Federated Learning using Mean Field Evolutionary Approach.- Chapter 7 Client Selection in Hierarchical Federated Learning with MeanField Game.- Chapter 8 Evolutionary Neural Architecture Search with Mean Field GameSelection Mechanism.- References.- Index.