Multi-Agent Federated Learning Using Covariance-Based Nearest Neighbor Gaussian Processes

Abstract

In this paper, we propose scalable methods for Gaussian process (GP) prediction in decentralized multi-agent systems. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. Moreover, we introduce a covariance-based nearest neighbor selection strategy that leverages cross-covariance similarity, enabling subsets of agents to make accurate predictions. The proposed decentralized schemes preserve the consistency properties of their centralized counterparts, while adhering to federated learning principles by restricting raw data exchange between agents. We validate the efficacy of the proposed decentralized algorithms with numerical experiments on real-world sea surface temperature and ground elevation map datasets across multiple fleet sizes.