Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: From Federated Learning to Federated Neural Architecture Search: A
  Survey   Federated learning is a recently proposed distributed machine learning
paradigm for privacy preservation, which has found a wide range of applications
where data privacy is of primary concern. Meanwhile, neural architecture search
has become very popular in deep learning for automatically tuning the
architecture and hyperparameters of deep neural networks. While both federated
learning and neural architecture search are faced with many open challenges,
searching for optimized neural architectures in the federated learning
framework is particularly demanding. This survey paper starts with a brief
introduction to federated learning, including both horizontal, vertical, and
hybrid federated learning. Then, neural architecture search approaches based on
reinforcement learning, evolutionary algorithms and gradient-based are
presented. This is followed by a description of federated neural architecture
search that has recently been proposed, which is categorized into online and
offline implementations, and single- and multi-objective search approaches.
Finally, remaining open research questions are outlined and promising research
topics are suggested.

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The categories of this paper are: hep-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex