Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with
  Sparsification   Federated learning is inherently vulnerable to model poisoning attacks
because its decentralized nature allows attackers to participate with
compromised devices. In model poisoning attacks, the attacker reduces the
model's performance on targeted sub-tasks (e.g. classifying planes as birds) by
uploading "poisoned" updates. In this report we introduce \algoname{}, a novel
defense that uses global top-k update sparsification and device-level gradient
clipping to mitigate model poisoning attacks. We propose a theoretical
framework for analyzing the robustness of defenses against poisoning attacks,
and provide robustness and convergence analysis of our algorithm. To validate
its empirical efficacy we conduct an open-source evaluation at scale across
multiple benchmark datasets for computer vision and federated learning.

32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg