A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
[article]
2019
arXiv
pre-print
The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. The model updates are sent back and aggregated on the server to update the master model then redistributed to the clients. In this paradigm, the user data never leaves the client, greatly
arXiv:1901.09888v1
fatcat:77g4p7rhsbcktoojr6p4ji3acq