Communication-Efficient Wireless Traffic Prediction with Federated Learning

Fuwei Gao, Chuanting Zhang (), Jingping Qiao, Kaiqiang Li and Yi Cao
Additional contact information
Fuwei Gao: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Chuanting Zhang: School of Software, Shandong University, Jinan 250100, China
Jingping Qiao: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Kaiqiang Li: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Yi Cao: School of Information Science and Engineering, University of Jinan, Jinan 250022, China

Mathematics, 2024, vol. 12, issue 16, 1-14

Abstract: Wireless traffic prediction is essential to developing intelligent communication networks that facilitate efficient resource allocation. Along this line, decentralized wireless traffic prediction under the paradigm of federated learning is becoming increasingly significant. Compared to traditional centralized learning, federated learning satisfies network operators’ requirements for sensitive data protection and reduces the consumption of network resources. In this paper, we propose a novel communication-efficient federated learning framework, named FedCE, by developing a gradient compression scheme and an adaptive aggregation strategy for wireless traffic prediction. FedCE achieves gradient compression through top-K sparsification and can largely relieve the communication burdens between local clients and the central server, making it communication-efficient. An adaptive aggregation strategy is designed by quantifying the different contributions of local models to the global model, making FedCE aware of spatial dependencies among various local clients. We validate the effectiveness of FedCE on two real-world datasets. The results demonstrate that FedCE can improve prediction accuracy by approximately 27% with only 20% of communications in the baseline method.

Keywords: wireless traffic prediction; spatial–temporal data analysis; federated learning; deep neural networks; gradient compression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/16/2539/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/16/2539/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:16:p:2539-:d:1458205

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().