Recent advances and future challenges in federated recommender systems
Marko Harasic, Felix-Sebastian Keese, Denny Mattern, Adrian Paschke
Abstract
Abstract Recommender systems are an integral part of modern-day user experience. They understand their preferences and support them in discovering meaningful content by creating personalized recommendations. With governmental regulations and growing users’ privacy awareness, capturing the required data is a challenging task today. Federated learning is a novel approach for distributed machine learning, which keeps users’ privacy in mind. In federated learning, the participating peers train a global model together, but personal data never leave the device or silo. Recently, the combination of recommender systems and federated learning gained a growing interest in the research community. A new recommender type named federated recommender system was created. This survey presents a comprehensive overview of current research in that field, including federated algorithms, architectural designs, and privacy mechanisms in the federated setting. Furthermore, it points out recent challenges and interesting future directions for further research.