Byzantine-resilient federated online learning for Gaussian process regression

• URL: http://arxiv.org/abs/2507.14021v1
• Date: Fri, 18 Jul 2025 15:39:47 GMT
• Title: Byzantine-resilient federated online learning for Gaussian process regression
• Authors: Xu Zhang, Zhenyuan Yuan, Minghui Zhu,
• Abstract summary: We develop a Byzantine-resilient federated GPR algorithm that allows a cloud and a group of agents to collaboratively learn a latent function.<n>Agent-based fused GPR refines local predictions by fusing the received global model with that of the agent-based local GPR.<n>We quantify the learning accuracy improvements of the agent-based fused GPR over the agent-based local GPR.
• Score: 10.8159638645264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study Byzantine-resilient federated online learning for Gaussian process regression (GPR). We develop a Byzantine-resilient federated GPR algorithm that allows a cloud and a group of agents to collaboratively learn a latent function and improve the learning performances where some agents exhibit Byzantine failures, i.e., arbitrary and potentially adversarial behavior. Each agent-based local GPR sends potentially compromised local predictions to the cloud, and the cloud-based aggregated GPR computes a global model by a Byzantine-resilient product of experts aggregation rule. Then the cloud broadcasts the current global model to all the agents. Agent-based fused GPR refines local predictions by fusing the received global model with that of the agent-based local GPR. Moreover, we quantify the learning accuracy improvements of the agent-based fused GPR over the agent-based local GPR. Experiments on a toy example and two medium-scale real-world datasets are conducted to demonstrate the performances of the proposed algorithm.

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