Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Private Dataset Generation Using Privacy Preserving Collaborative
  Learning   With increasing usage of deep learning algorithms in many application, new
research questions related to privacy and adversarial attacks are emerging.
However, the deep learning algorithm improvement needs more and more data to be
shared within research community. Methodologies like federated learning,
differential privacy, additive secret sharing provides a way to train machine
learning models on edge without moving the data from the edge. However, it is
very computationally intensive and prone to adversarial attacks. Therefore,
this work introduces a privacy preserving FedCollabNN framework for training
machine learning models at edge, which is computationally efficient and robust
against adversarial attacks. The simulation results using MNIST dataset
indicates the effectiveness of the framework.

34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th