In this paper, we consider Byzantine-tolerant federated learning for streaming data using Gaussian process regression (GPR). In particular, a cloud and a group of agents aim to collaboratively learn a latent function where some agents are subject to Byzantine attacks. We develop a Byzantine-tolerant federated GPR algorithm, which includes three modules: agent-based local GPR, cloud-based aggregated GPR and agent-based fused GPR. We derive the upper bounds on prediction error between the mean from the cloud-based aggregated GPR and the target function provided that Byzantine agents are less than one quarter of all the agents. We also characterize the lower and upper bounds of the predictive variance. Experiments on a synthetic dataset and two real-world datasets are conducted to evaluate the proposed algorithm.
Xu Zhang (Pennsylvania State University)
Secure control and learning
Zhenyuan Yuan (Pennsylvania State University)
Zhenyuan Yuan is a Ph.D. candidate in the School of Electrical Engineering and Computer Science at the Pennsylvania State University. He received B.S. in Electrical Engineering and B.S. in Mathematics from the Pennsylvania State University in 2018. His research interests lie in machine learning and motion planning with applications in robotic networks. He is a recipient of the Rudolf Kalman Best Paper Award of the ASME Journal of Dynamic Systems Measurement and Control in 2019 and the Penn State Alumni Association Scholarship for Penn State Alumni in the Graduate School in 2021.
Minghui Zhu (Pennsylvania State University)
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