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Coded Distributed Gaussian Process Regression
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DOI：10.1109/LCOMM.2022.3208969.png)
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Abstract
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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.
Keywords:
Training
Costs
Distributed databases
Gaussian processes
Transforms
Load management
Computational modeling
Gaussian process regression
coded load balancing
heterogeneous wireless networks
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