Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Energy Demand Prediction with Federated Learning for Electric Vehicle
  Networks   In this paper, we propose novel approaches using state-of-the-art machine
learning techniques, aiming at predicting energy demand for electric vehicle
(EV) networks. These methods can learn and find the correlation of complex
hidden features to improve the prediction accuracy. First, we propose an energy
demand learning (EDL)-based prediction solution in which a charging station
provider (CSP) gathers information from all charging stations (CSs) and then
performs the EDL algorithm to predict the energy demand for the considered
area. However, this approach requires frequent data sharing between the CSs and
the CSP, thereby driving communication overhead and privacy issues for the EVs
and CSs. To address this problem, we propose a federated energy demand learning
(FEDL) approach which allows the CSs sharing their information without
revealing real datasets. Specifically, the CSs only need to send their trained
models to the CSP for processing. In this case, we can significantly reduce the
communication overhead and effectively protect data privacy for the EV users.
To further improve the effectiveness of the FEDL, we then introduce a novel
clustering-based EDL approach for EV networks by grouping the CSs into clusters
before applying the EDL algorithms. Through experimental results, we show that
our proposed approaches can improve the accuracy of energy demand prediction up
to 24.63% and decrease communication overhead by 83.4% compared with other
baseline machine learning algorithms.

7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph