ine learning. A direct application is wireless federated learning. Chapter 10 provides an overview for secure spectrum sharing with machine learning. While secure spectrum sharing is not a new topic, we have witnessed advancements in this area, especially with machine learning techniques. Chapter 11 presents detailed machine learning methodologies for secure machine learning. This chapter illustrates several dominant attacks and their respective mitigation approaches. Chapter 12 concludes this book and gives some emerging research directions in 5G and beyond wireless networks. We hope our readers will enjoy this book. January 2023 Haijian Sun, University of Georgia Rose Qingyang Hu, Utah State University Yi Qian, University of Nebraska-Lincoln Acknowledgments We would like to thank our families for their continuous support and love. We would like to express our sincere gratitude and appreciation to our colleagues, students, and staff at University of Georgia, Utah State University, and University of Nebraska-Lincoln who have supported us throughout the journey of writing and publish- ing this book. Your encouragement, feedback, and advice have been invaluable in shaping the final product, and we are truly grateful for your contributions. We also would like to extend our heartfelt gratitude to Juliet Booker, Sandra Grayson, and Becky Cowan at Wiley who have played an integral role in bringing our new book to fruition. Thank you for your hard work, support, and guidance throughout the publishing process. This book project was partially supported by the U.S. National Science Foundation under grants CNS-2236449, CNS-2007995, CNS-2008145, ECCS-2139508, and ECCS-2139520. Haijian Sun, Rose Qingyang Hu, and Yi Qian Introduction to 5G and Beyond Network We have witnessed an unprecedented development of wireless technology for the past few decades. Starting from 1980s, when the first mobile phone was released, major wireless technology advanced almost every decade. From first generation (1G) to 4G. The invention of smart d