A Generic Federated Recommendation Framework via Fake Marks and Secret Sharing

With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect users’ data. However, a typical machine learning-based recommendation algorithm requires the data to learn users’ preferences. Some recent works thus turn to develop federated learning-based recommendation algorithms, but most of them either cannot protect the users’ privacy well, or sacrifice the model accuracy. In this article, we propose a lossless and generic federated recommendation framework via fake marks and secret sharing (FMSS). Our FMSS can not only protect the two types of users’ privacy, i.e., rating values and rating behaviors, without sacrificing the recommendation performance, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation. Specifically, we extend existing fake items to fake marks, and combine it with secret sharing to perturb the data uploaded by the clients to a server. We then apply our FMSS to six representative recommendation algorithms, i.e., MF-MPC and NeuMF for rating prediction, eALS and VAE-CF for item ranking, and Fossil and GRU4Rec for sequential recommendation. The experimental results demonstrate that our FMSS is a lossless and generic framework, which is able to federate a series of different recommendation algorithms in a lossless and privacy-aware manner.