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Privacy Threats Against Federated Matrix Factorization
[article]
2020
arXiv
pre-print
Matrix Factorization has been very successful in practical recommendation applications and e-commerce. Due to data shortage and stringent regulations, it can be hard to collect sufficient data to build performant recommender systems for a single company. Federated learning provides the possibility to bridge the data silos and build machine learning models without compromising privacy and security. Participants sharing common users or items collaboratively build a model over data from all the
arXiv:2007.01587v1
fatcat:kevu5w6hxrc2vkzfwhvin6imhu