  Various attack methods against recommender systems have been proposed in the
past years, and the security issues of recommender systems have drawn
considerable attention. Traditional attacks attempt to make target items
recommended to as many users as possible by poisoning the training data.
Benifiting from the feature of protecting users' private data, federated
recommendation can effectively defend such attacks. Therefore, quite a few
works have devoted themselves to developing federated recommender systems. For
proving current federated recommendation is still vulnerable, in this work we
probe to design attack approaches targeting deep learning based recommender
models in federated learning scenarios. Specifically, our attacks generate
poisoned gradients for manipulated malicious users to upload based on two
strategies (i.e., random approximation and hard user mining). Extensive
experiments show that our well-designed attacks can effectively poison the
target models, and the attack effectiveness sets the state-of-the-art.
