Cross-device Federated Recommendation Privacy-Preserving Personalization

This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing paradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant.

This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point.

This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence.

Chapter 1. Introduction.- Chapter 2. Learning Paradigms in Cross-device Federated Recommendation.- Chapter 3. Privacy Computing in Cross-device Federated Recommendation.- Chapter 4. Federated Issues in Cross-device Federated Recommendation.- Chapter 5. Future Prospects.

Xiangjie Kong received the B.Sc. and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 2004 and 2009, respectively. He is a professor with College of Computer Science and Technology, Zhejiang University of Technology, China. Previously, he was an associate professor with the School of Software, Dalian University of Technology, China. He has published over 200 scientific papers in international journals and conferences (with over 180 indexed by ISI SCIE). His research interests include social computing, mobile computing, and data science. He is a senior member of the IEEE, a distinguished member of CCF, and a member of ACM.

Lingyun Wang received his Master degree from College of Computer Science and Technology, Zhejiang University of Technology, China, in 2024. His main research interests are recommender systems, federated learning, and knowledge discovery.

Mengmeng Wang received the PhD degree in control science and engineering from Zhejiang University in 2024. She is currently an assistant professor in the College of Computer Science and Technology, Zhejiang University of Technology. Her research interests include image/video understanding, text-to-video/image-to-video generation, computer vision, robotics, and intelligent transportation systems.

Guojiang Shen received the BSc degree in Control Theory and Control Engineering and the PhD degree in Control Science and Engineering from Zhejiang University, Hangzhou, China, in 1999 and 2004, respectively. He is currently a professor in the College of Computer Science and Technology, Zhejiang University of Technology. His current research interests include artificial intelligence, Big Data analytics, and intelligent transportation systems.