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They understand their preferences and support them in discovering meaningful content by creating personalized recommendations. With governmental regulations and growing users\u2019 privacy awareness, capturing the required data is a challenging task today. Federated learning is a novel approach for distributed machine learning, which keeps users\u2019 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. 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