Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fishing for User Data in Large-Batch Federated Learning via Gradient
  Magnification   Federated learning (FL) has rapidly risen in popularity due to its promise of
privacy and efficiency. Previous works have exposed privacy vulnerabilities in
the FL pipeline by recovering user data from gradient updates. However,
existing attacks fail to address realistic settings because they either 1)
require a `toy' settings with very small batch sizes, or 2) require unrealistic
and conspicuous architecture modifications. We introduce a new strategy that
dramatically elevates existing attacks to operate on batches of arbitrarily
large size, and without architectural modifications. Our model-agnostic
strategy only requires modifications to the model parameters sent to the user,
which is a realistic threat model in many scenarios. We demonstrate the
strategy in challenging large-scale settings, obtaining high-fidelity data
extraction in both cross-device and cross-silo federated learning.

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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat