Navigating threats in federated recommender systems: a survey of attack models and defense strategies

Federated Learning (FL) has emerged as a prominent solution for privacy-preserving and efficient learning mechanisms. However, recent investigations have highlighted that the security vulnerabilities of FL remain largely unexplored in Recommender Systems (RS). Research on attack models is significant due to its direct impact on the security of decentralized recommendation algorithms. This survey systematically identifies and analyzes the unique vulnerabilities of Federated Recommender Systems (FedRS) under both classical and Artificial Intelligence (AI)-driven attack models, and presents a structured taxonomy along with synthesized insights that reveal how such threats impact system integrity, user trust, and financial outcomes in recommendation-driven industries. This critical gap is addressed through a comprehensive analysis of attack models and state-of-the-art defense strategies in FedRS. Existing attack possibilities in RS, and specifically in FedRS, are collected and analyzed from multiple perspectives. Additionally, vulnerabilities identified in mainstream FL research are examined for their impact within the RS context. Based on this systematic investigation, a novel taxonomy of threat and defense models is proposed, offering a structured classification of attack vectors and an in-depth examination of corresponding defense strategies. This study covers a wide range of attack types, including recent AI-empowered federated attack models and classical attacks, and provides insights into the ethical implications and associated business risks. In addition to the theoretical investigation, a public repository of relevant codebases, metrics and datasets has been compiled to promote future research and facilitate reproducibility in this rapidly evolving domain.