Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Layer-wise Characterization of Latent Information Leakage in Federated
  Learning   Training deep neural networks via federated learning allows clients to share,
instead of the original data, only the model trained on their data. Prior work
has demonstrated that in practice a client's private information, unrelated to
the main learning task, can be discovered from the model's gradients, which
compromises the promised privacy protection. However, there is still no formal
approach for quantifying the leakage of private information via the shared
updated model or gradients. In this work, we analyze property inference attacks
and define two metrics based on (i) an adaptation of the empirical
$\mathcal{V}$-information, and (ii) a sensitivity analysis using Jacobian
matrices allowing us to measure changes in the gradients with respect to latent
information. We show the applicability of our proposed metrics in localizing
private latent information in a layer-wise manner and in two settings where (i)
we have or (ii) we do not have knowledge of the attackers' capabilities. We
evaluate the proposed metrics for quantifying information leakage on three
real-world datasets using three benchmark models.

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The categories of this paper are: eess
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
2
The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th