Federated Gaussian Process Learning via Pseudo-Representations for Large-Scale Multi-Robot Systems
2026-02-12 • Multiagent Systems
Multiagent Systems
AI summaryⓘ
The authors developed a new method called pxpGP to help groups of robots understand complex environments better without using too much computing power or communication. They improved a common technique called Gaussian Processes, which usually gets slow when dealing with many data points, by making a smart way to summarize information locally and then combine it efficiently across robots. Their method works both in setups with a central controller and fully decentralized ones. Tests with simulated and real data showed their approach predicts better and scales well compared to older methods.
Multi-robot systemsGaussian ProcessesSparse variational inferenceDistributed learningConsensus ADMMDecentralized networksHyperparameter estimationScalable algorithmsPseudo-representationProximal optimization
Authors
Sanket A. Salunkhe, George P. Kontoudis
Abstract
Multi-robot systems require scalable and federated methods to model complex environments under computational and communication constraints. Gaussian Processes (GPs) offer robust probabilistic modeling, but suffer from cubic computational complexity, limiting their applicability in large-scale deployments. To address this challenge, we introduce the pxpGP, a novel distributed GP framework tailored for both centralized and decentralized large-scale multi-robot networks. Our approach leverages sparse variational inference to generate a local compact pseudo-representation. We introduce a sparse variational optimization scheme that bounds local pseudo-datasets and formulate a global scaled proximal-inexact consensus alternating direction method of multipliers (ADMM) with adaptive parameter updates and warm-start initialization. Experiments on synthetic and real-world datasets demonstrate that pxpGP and its decentralized variant, dec-pxpGP, outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in large-scale networks.