mlsanityData to 7 Oct 2026

Byzantine-resilient federated online learning for Gaussian process regression

arXiv 2507.14021 ↗First posted 18 Jul 2025cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

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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References · 16

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 698
  4. 2019Federated Machine Learning: Concept and Applicationscited by 409
  5. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
  6. 2018When Gaussian Process Meets Big Data: A Review of Scalable GPscited by 205
  7. 2018RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasetscited by 148
  8. 2020Learning from History for Byzantine Robust Optimizationcited by 137
  9. 2020Byzantine-Robust Learning on Heterogeneous Datasets via Bucketingcited by 125
  10. 2020Byzantine-Resilient Secure Federated Learningcited by 93
  11. 2023Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneitycited by 64
  12. 2022Byzantine Machine Learning Made Easy by Resilient Averaging of Momentumscited by 63
4 more
  1. 2018Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regressioncited by 33
  2. 2020Byzantine-Resilient High-Dimensional Federated Learningcited by 27
  3. 2021Lightweight Distributed Gaussian Process Regression for Online Machine Learningcited by 4
  4. 2024Federated reinforcement learning for robot motion planning with zero-shot generalizationcited by 3

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