Challenges and Advancement in Federated Recommendation System: A Comprehensive Review

In the age of information overload, recommendation systems have become essential for individuals and society. Deep learning has been successful in developing high-quality recommendation systems, but limited training data can hinder their effectiveness. Collecting more data may compromise user privacy and security. To address this issue and improve recommendation quality, federated machine learning, and recommender systems have been fused into a new research area called federated recommender systems. While researchers have made progress in this area, gaps still exist. This review aims to (1) identify the common challenges faced by federated recommendation systems; (2) explore different types of federated recommendation systems based on architecture and machine learning models used; (3) conduct a comparative analysis of federated recommendation systems; (4) examine the communication efficiency, tools, and frameworks used for federated recommendation systems; and (5) suggest future research directions for practitioners in federated learning.