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Название: Cross-device Federated Recommendation: Privacy-Preserving PersonalizationАвтор: Xiangjie Kong, Lingyun Wang, Mengmeng Wang, Guojiang Shen
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2025
Страниц: 166
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
This book introduces the prevailing domains of recommender systems and cross-device Federated Learning (FL), 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 (ML), 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.
