Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient
  Ensembling   Recent advances in federated learning have demonstrated its promising
capability to learn on decentralized datasets. However, a considerable amount
of work has raised concerns due to the potential risks of adversaries
participating in the framework to poison the global model for an adversarial
purpose. This paper investigates the feasibility of model poisoning for
backdoor attacks through \textit{rare word embeddings of NLP models} in text
classification and sequence-to-sequence tasks. In text classification, less
than 1\% of adversary clients suffices to manipulate the model output without
any drop in the performance of clean sentences. For a less complex dataset, a
mere 0.1\% of adversary clients is enough to poison the global model
effectively. We also propose a technique specialized in the federated learning
scheme called gradient ensemble, which enhances the backdoor performance in all
experimental settings.

19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex