Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resource Consumption for Supporting Federated Learning in Wireless
  Networks   Federated learning (FL) has recently become one of the hottest focuses in
wireless edge networks with the ever-increasing computing capability of user
equipment (UE). In FL, UEs train local machine learning models and transmit
them to an aggregator, where a global model is formed and then sent back to
UEs. In wireless networks, local training and model transmission can be
unsuccessful due to constrained computing resources, wireless channel
impairments, bandwidth limitations, etc., which degrades FL performance in
model accuracy and/or training time. Moreover, we need to quantify the benefits
and cost of deploying edge intelligence, as model training and transmission
consume certain amount of resources. Therefore, it is imperative to deeply
understand the relationship between FL performance and multiple-dimensional
resources. In this paper, we construct an analytical model to investigate the
relationship between the FL model accuracy and consumed resources in FL
empowered wireless edge networks. Based on the analytical model, we explicitly
quantify the model accuracy, available computing resources and communication
resources. Numerical results validate the effectiveness of our theoretical
modeling and analysis, and demonstrate the trade-off between the communication
and computing resources for achieving a certain model accuracy.

2
The categories of this paper are: math
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an