Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PipAttack: Poisoning Federated Recommender Systems forManipulating Item
  Promotion   Due to the growing privacy concerns, decentralization emerges rapidly in
personalized services, especially recommendation. Also, recent studies have
shown that centralized models are vulnerable to poisoning attacks, compromising
their integrity. In the context of recommender systems, a typical goal of such
poisoning attacks is to promote the adversary's target items by interfering
with the training dataset and/or process. Hence, a common practice is to
subsume recommender systems under the decentralized federated learning
paradigm, which enables all user devices to collaboratively learn a global
recommender while retaining all the sensitive data locally. Without exposing
the full knowledge of the recommender and entire dataset to end-users, such
federated recommendation is widely regarded `safe' towards poisoning attacks.
In this paper, we present a systematic approach to backdooring federated
recommender systems for targeted item promotion. The core tactic is to take
advantage of the inherent popularity bias that commonly exists in data-driven
recommenders. As popular items are more likely to appear in the recommendation
list, our innovatively designed attack model enables the target item to have
the characteristics of popular items in the embedding space. Then, by uploading
carefully crafted gradients via a small number of malicious users during the
model update, we can effectively increase the exposure rate of a target
(unpopular) item in the resulted federated recommender. Evaluations on two
real-world datasets show that 1) our attack model significantly boosts the
exposure rate of the target item in a stealthy way, without harming the
accuracy of the poisoned recommender; and 2) existing defenses are not
effective enough, highlighting the need for new defenses against our local
model poisoning attacks to federated recommender systems.

6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat