Comparison of Unilateral Algorithms Based on Federated Learning in Smart Cities

DOI

10.2991/978-94-6463-230-9_13How to use a DOI?

Keywords

Federal learning; Machine learning; Smart city; Air quality

Abstract

With the continuous popularization of the concept of smart cities, the environmental issues in smart cities have also received extensive attention. An important indicator reflecting the environmental problems in smart cities is the concentration of PM2.5 in the air. At the same time, we use a federated learning framework and try to select the appropriate algorithm in the federated learning edge learning device of the scene. For this reason, we compare decision tree algorithm, Gaussian regression algorithm, support vector machine algorithm, neural network algorithm, etc. Finally, through modeling and experiments, it is concluded that using Gaussian regression algorithm on edge devices is more effective.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zutao Zhang
AU  - Junhong Lai
AU  - Fangze Cao
AU  - Yutong Guan
AU  - Qian Zhu
PY  - 2023
DA  - 2023/09/04
TI  - Comparison of Unilateral Algorithms Based on Federated Learning in Smart Cities
BT  - Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023)
PB  - Atlantis Press
SP  - 106
EP  - 111
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-230-9_13
DO  - 10.2991/978-94-6463-230-9_13
ID  - Zhang2023
ER  -