Coded Distributed Gaussian Process Regression

In this letter, we propose a coded load balancing method for distributed Gaussian process regression over heterogeneous wireless networks, where users with diverse computational and communications capabilities may offload excessive training data onto a computationally stronger central server to redu...

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Bibliographic Details
Published inIEEE communications letters Vol. 27; no. 1; pp. 372 - 376
Main Authors Zeulin, Nikita, Galinina, Olga, Himayat, Nageen, Andreev, Sergey
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
coded load balancing
Computational modeling
Costs
Distributed databases
Gaussian process
Gaussian process regression
Gaussian processes
heterogeneous wireless networks
Load management
Training
Transforms
Wireless networks
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Summary:In this letter, we propose a coded load balancing method for distributed Gaussian process regression over heterogeneous wireless networks, where users with diverse computational and communications capabilities may offload excessive training data onto a computationally stronger central server to reduce collaborative processing times. The offloaded data are transformed using random Fourier feature mapping and encoded with a random orthogonal matrix to prevent transmission of raw data. The proposed method is particularly applicable to compute-intensive applications, where users operate with large datasets.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3208969