  Federated recommendation (FedRec) can train personalized recommenders without
collecting user data, but the decentralized nature makes it susceptible to
poisoning attacks. Most previous studies focus on the targeted attack to
promote certain items, while the untargeted attack that aims to degrade the
overall performance of the FedRec system remains less explored. In fact,
untargeted attacks can disrupt the user experience and bring severe financial
loss to the service provider. However, existing untargeted attack methods are
either inapplicable or ineffective against FedRec systems. In this paper, we
delve into the untargeted attack and its defense for FedRec systems. (i) We
propose ClusterAttack, a novel untargeted attack method. It uploads poisonous
gradients that converge the item embeddings into several dense clusters, which
make the recommender generate similar scores for these items in the same
cluster and perturb the ranking order. (ii) We propose a uniformity-based
defense mechanism (UNION) to protect FedRec systems from such attacks. We
design a contrastive learning task that regularizes the item embeddings toward
a uniform distribution. Then the server filters out these malicious gradients
by estimating the uniformity of updated item embeddings. Experiments on two
public datasets show that ClusterAttack can effectively degrade the performance
of FedRec systems while circumventing many defense methods, and UNION can
improve the resistance of the system against various untargeted attacks,
including our ClusterAttack.
