mlsanityData to 7 Oct 2026

Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning

arXiv 2203.02865 ↗First posted 6 Mar 2022stat.ML cs.LG cs.MAcited by 4

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Abstract (arXiv)

In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP training optimization algorithms, we employ the alternating direction method of multipliers (ADMM). A closed-form solution of the decentralized proximal ADMM is provided for the case of GP hyper-parameter training with maximum likelihood estimation. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. In addition, we propose a covariance-based nearest neighbor selection strategy that enables a subset of agents to perform predictions. The efficacy of the proposed methods is illustrated with numerical experiments on synthetic and real data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 24 of its 72 references that are in the map. First posted on arXiv 6 Mar 2022.

Builds on

24 of 72 references.

References · 24

  1. 2021Convex optimizationcited by 2,479
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2013Gaussian Processes for Big Datacited by 377
  4. 2018When Gaussian Process Meets Big Data: A Review of Scalable GPscited by 205
  5. 2015Distributed Gaussian Processescited by 84
  6. 2014Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasetscited by 71
  7. 2014Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictionscited by 66
  8. 2014Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problemscited by 64
  9. 2013On the Linear Convergence of the ADMM in Decentralized Consensus Optimizationcited by 57
  10. 2018Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regressioncited by 33
  11. 2019GADMM: Fast and Communication Efficient Framework for Distributed Machine Learningcited by 30
  12. 2014Multi-Agent Distributed Optimization via Inexact Consensus ADMMcited by 21
12 more
  1. 2016Nested Kriging predictions for datasets with large number of observationscited by 18
  2. 2014Efficient Informative Sensing using Multiple Robotscited by 16
  3. 2016Convergence Rate of Distributed ADMM over Networkscited by 16
  4. 2014Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regressioncited by 15
  5. 2017Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Modelscited by 11
  6. 2013Gaussian Processes for Nonlinear Signal Processingcited by 9
  7. 2019Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verificationcited by 7
  8. 2021Numerical issues in maximum likelihood parameter estimation for Gaussian process interpolationcited by 7
  9. 2021A Survey of Distributed Optimization Methods for Multi-Robot Systemscited by 7
  10. 2017Learning of Gaussian Processes in Distributed and Communication Limited Systemscited by 5
  11. 2016How priors of initial hyperparameters affect Gaussian process regression modelscited by 3
  12. 2019Learning a Spatial Field in Minimum Time with a Team of Robotscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.

4 papers.

  1. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  2. 2023Practical Privacy-Preserving Gaussian Process Regression via Secret Sharingcited by 2
  3. 2025A Spatially Informed Gaussian Process UCB Method for Decentralized Coverage Controlcited by 0
  4. 2026Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systemscited by 0