Federated Gaussian Process Learning via Pseudo-Representations for Large-Scale Multi-Robot Systems

Sanket A Salunkhe, George P. Kontoudis

Keywords: Gaussian Processes, Multi-Robot Systems, Distributed Optimization, Sparse Methods, Federated Learning, Large-Scale Networks

TL;DR: We propose pxpGP, a scalable federated Gaussian process framework that uses privacy-preserving local pseudo-data and distributed consensus optimization to enable accurate and efficient learning in large-scale multi-robot systems.

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.

Area: Robotics and Control (ROBOT)

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Submission Number: 1318