Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the effect of normalization layers on Differentially Private training
  of deep Neural networks   Differentially private stochastic gradient descent (DPSGD) is a variation of
stochastic gradient descent based on the Differential Privacy (DP) paradigm,
which can mitigate privacy threats that arise from the presence of sensitive
information in training data. However, one major drawback of training deep
neural networks with DPSGD is a reduction in the models accuracy. In this
paper, we study the effect of normalization layers on the performance of DPSGD.
We demonstrate that normalization layers significantly impact the utility of
deep neural networks with noisy parameters and should be considered essential
ingredients of training with DPSGD. In particular, we propose a novel method
for integrating batch normalization with DPSGD without incurring an additional
privacy loss. With our approach, we are able to train deeper networks and
achieve a better utility-privacy trade-off.

7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math