  Federated recommendation systems can provide good performance without
collecting users' private data, making them attractive. However, they are
susceptible to low-cost poisoning attacks that can degrade their performance.
In this paper, we develop a novel federated recommendation technique that is
robust against the poisoning attack where Byzantine clients prevail. We argue
that the key to Byzantine detection is monitoring of gradients of the model
parameters of clients. We then propose a robust learning strategy where instead
of using model parameters, the central server computes and utilizes the
gradients to filter out Byzantine clients. Theoretically, we justify our robust
learning strategy by our proposed definition of Byzantine resilience.
Empirically, we confirm the efficacy of our robust learning strategy employing
four datasets in a federated recommendation system.
