Coded Distributed Gaussian Process Regression

Nikita Zeulin

doi:10.1109/LCOMM.2022.3208969

2023-01-01

DOI：10.1109/LCOMM.2022.3208969

原文链接

摘要

En 中文

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.

Keyword:

Training

Costs

Distributed databases

Gaussian processes

Transforms

Load management

Computational modeling

Gaussian process regression

coded load balancing

heterogeneous wireless networks

期刊

IEEE Communications Letters

IF:

4.4

论文数:

1.3W

被引数:

2.2W

机构

intel corporation

学者数:

论文数: 22

被引数: 0

tampere university

学者数:

481

论文数: 184

被引数: 0

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