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

2026-02-13 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, extended

Summary

A novel distributed Gaussian Process (GP) framework, pxpGP, and its decentralized variant, dec-pxpGP, have been introduced to address the computational and communication challenges of large-scale multi-robot systems. These methods leverage sparse variational inference to generate compact, local pseudo-representations, which are then shared among agents instead of raw data, ensuring data privacy. The framework employs a scaled proximal-inexact consensus ADMM algorithm with adaptive parameter updates and warm-start initialization to accelerate convergence. Experiments on synthetic datasets with $N=16,900$ and $N=34,900$ samples, and real-world NASA SRTM terrain datasets with $N=30,000$ training samples across fleet sizes from $M=16$ to $M=100$ agents, demonstrate that pxpGP and dec-pxpGP outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in larger networks, while maintaining numerical robustness.

Key takeaway

For AI Scientists and Research Scientists developing multi-robot systems that require robust probabilistic modeling under computational and privacy constraints, pxpGP and dec-pxpGP offer a superior approach. Your teams should consider adopting this framework to achieve more accurate hyperparameter estimation and predictive performance in large-scale deployments, especially where data privacy is paramount. This method's efficiency and scalability make it ideal for applications like cooperative exploration and environmental monitoring.

Key insights

pxpGP enables scalable, privacy-preserving Gaussian Process learning for large multi-robot systems via pseudo-representations and optimized ADMM.

Principles

• Share pseudo-representations, not raw data, for privacy.
• Regularize inducing points to prevent drift and clustering.
• Adaptive parameter tuning and warm-starts accelerate convergence.

Method

Each agent generates a local sparse GP pseudo-dataset, which is then shared and merged to form a pseudo-augmented dataset. A scaled proximal-inexact consensus ADMM, with warm-start and adaptive parameter updates, optimizes global hyperparameters.

In practice

• Use pxpGP for multi-robot environmental monitoring.
• Apply dec-pxpGP for decentralized collaborative exploration.
• Implement boundary and repulsive penalties for stable GP models.

Topics

• Federated Learning
• Gaussian Processes
• Multi-Robot Systems
• Distributed Optimization
• Sparse Variational Inference

Code references

• mpala-lab/distributed-gaussian-processes

Best for: AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.