Название: Cross-device Federated Recommendation: Privacy-Preserving Personalization
Автор: 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. 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.

The Chapter 1 introduces Federated Learning (FL), a distributed Machine Learning approach designed to address data privacy, security, and processing challenges in the era of Big Data and AI. FL allows model training across multiple devices or organizations without sharing raw data, thus enhancing privacy by only exchanging model updates. The chapter covers three FL modes: Horizontal Federated Learning (HFL), Vertical Federated Learning (VFL), and Transfer Federated Learning (TFL). It also explores recommender systems (RecSys), focusing on traditional methods like collaborative filtering and advanced techniques, such as graph-based approaches. Finally, the chapter discusses Federated Recommender Systems (FedRec), which integrate FL and recommender systems to improve privacy-preserving recommendation services while addressing challenges like data heterogeneity and communication overhead.

Contents: