Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning at the Network Edge: A Survey   Resource-constrained IoT devices, such as sensors and actuators, have become
ubiquitous in recent years. This has led to the generation of large quantities
of data in real-time, which is an appealing target for AI systems. However,
deploying machine learning models on such end-devices is nearly impossible. A
typical solution involves offloading data to external computing systems (such
as cloud servers) for further processing but this worsens latency, leads to
increased communication costs, and adds to privacy concerns. To address this
issue, efforts have been made to place additional computing devices at the edge
of the network, i.e close to the IoT devices where the data is generated.
Deploying machine learning systems on such edge computing devices alleviates
the above issues by allowing computations to be performed close to the data
sources. This survey describes major research efforts where machine learning
systems have been deployed at the edge of computer networks, focusing on the
operational aspects including compression techniques, tools, frameworks, and
hardware used in successful applications of intelligent edge systems.

8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con