Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Learning for Industrial Internet of Things in Future
  Industries   The Industrial Internet of Things (IIoT) offers promising opportunities to
transform the operation of industrial systems and becomes a key enabler for
future industries. Recently, artificial intelligence (AI) has been widely
utilized for realizing intelligent IIoT applications where AI techniques
require centralized data collection and processing. However, this is not always
feasible in realistic scenarios due to the high scalability of modern IIoT
networks and growing industrial data confidentiality. Federated Learning (FL),
as an emerging collaborative AI approach, is particularly attractive for
intelligent IIoT networks by coordinating multiple IIoT devices and machines to
perform AI training at the network edge while helping protect user privacy. In
this article, we provide a detailed overview and discussions of the emerging
applications of FL in key IIoT services and applications. A case study is also
provided to demonstrate the feasibility of FL in IIoT. Finally, we highlight a
range of interesting open research topics that need to be addressed for the
full realization of FL-IIoT in industries.

23
The categories of this paper are: solv-int
2
The categories of this paper are: math
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph