A Survey on Federated Recommendation Systems. (arXiv:2301.00767v1 [cs.IR])

Federated learning has recently been applied to recommendation systems to
protect user privacy. In federated learning settings, recommendation systems
can train recommendation models only collecting the intermediate parameters
instead of the real user data, which greatly enhances the user privacy. Beside,
federated recommendation systems enable to collaborate with other data
platforms to improve recommended model performance while meeting the regulation
and privacy constraints. However, federated recommendation systems faces many
new challenges such as privacy, security, heterogeneity and communication
costs. While …

collecting data enable federated learning platforms privacy protect settings survey systems train user data user privacy