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 significant research has been conducted in these areas, gaps in
the surveying literature still exist. In this survey, we-(1) summarize some
common privacy mechanisms used in federated recommendation systems and discuss
the advantages and limitations of each mechanism; (2) review some robust
aggregation strategies and several novel attacks against security; (3)
summarize some approaches to address heterogeneity and communication costs
problems; (4)introduce some open source platforms that can be used to build
federated recommendation systems; (5) present some prospective research
directions in the future. This survey can guide researchers and practitioners
understand the research progress in these areas.

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