Decentralized Gaussian Processess for Multi-Agent Systems

Demonstration code of decentralized Gaussian process training [1,2] and prediction [3,4]. For further details check the paper for decentralized GP training here and prediction here. A presentation of the decentralized GP training paper can be found here and prediction here.

Content

The code implements:

• Centralized Generalized Analytical Proximal GP Training (gapx-GP) [1]
• Decentralized Consensus GP Training (DEC-c-GP) [1]
• Decentralized Analytical Proximal GP Training (DEC-apx-GP) [2]
• Decentralized Generalized Analytical Proximal GP Training (DEC-gapx-GP) [1]
• Decentralized Nested Pointwise Aggregation of Experts (DEC-NPAE) [3]
• Distributed Nested Pointwise Aggregation of Experts (DIST-NPAE) [3]
• Decentralized Nested Pointwise Aggregation of Experts * (DEC-NPAE*) [4]
• Decentralized Product of Experts (DEC-PoE) [4]
• Decentralized Generalized Product of Experts (DEC-gPoE) [4]
• Decentralized Bayesian Committee Machine (DEC-BCM) [4]
• Decentralized Robust Bayesian Committee Machine (DEC-rBCM) [4]
• Decentralized Generalized Robust Bayesian Committee Machine (DEC-grBCM) [4]
• Decentralized Neaerest Neighbor Product of Experts (DEC-NN-PoE) [4]
• Decentralized Neaerest Neighbor Generalized Product of Experts (DEC-NN-gPoE) [4]
• Decentralized Nearest Neighbor Bayesian Committee Machine (DEC-NN-BCM) [4]
• Decentralized Nearest Neighbor Robust Bayesian Committee Machine (DEC-NN-rBCM) [4]
• Decentralized Nearest Neighbor Generalized Robust Bayesian Committee Machine (DEC-NN-grBCM) [4]

The source code of the factorized training and the centralized NPAE [5] can be found in the GRBCM [6] GitHub repository.

Dependencies

gpml toolbox

Execute: For decentralized GP training

demo_2D_train.m

For decentralized GP prediction

demo_2D_pred.m

References

[1] G. P. Kontoudis and D. J. Stilwell, “Scalable, Federated Gaussian Process Training for Decentralized Multi-Agent Systems,” in IEEE Access, 2024.

[2] G. P. Kontoudis and D. J. Stilwell, “Decentralized Federated Learning using Gaussian Processes,” in IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2023.

[3] G. P. Kontoudis and D. J. Stilwell, “Decentralized Nested Gaussian Processes for Multi-Robot Systems,” in IEEE International Conference on Robotics and Automation (ICRA), 2021.

[4] G. P. Kontoudis and D. J. Stilwell, “Multi-Agent Federated Learning using Covariance-Based Nearest Neighbor Gaussian Processes,” in IEEE Transactions on Machine Learning in Communications and Networking (TMLCN), 2025.

[5] D. Rullière, N. Durrande, F. Bachoc, and C. Chevalier, “Nested Kriging predictions for datasets with a large number of observations,” Statistics and Computing, 2018.

[6] H. Liu, J. Cai, Y. Wang, and Y. S. Ong, “Generalized robust Bayesian committee machine for large-scale Gaussian process regression,” in International Conference on Machine Learning (ICML), 2018.

Notes

Please open a GitHub issue if you encounter any problem.