Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Byzantine-tolerant federated Gaussian process regression for streaming data
Authors: Xu Zhang, Zhenyuan Yuan, Minghui Zhu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a synthetic dataset and two real-world datasets are conducted to evaluate the proposed algorithm. |
| Researcher Affiliation | Academia | Xu Zhang Pennsylvania State University EMAIL Zhenyuan Yuan Pennsylvania State University EMAIL Minghui Zhu Pennsylvania State University EMAIL |
| Pseudocode | Yes | Algorithm 1 Byzantine-tolerant federated GPR, Algorithm 2 Agent-based local GPR: l GPR(D[i](t)), Algorithm 3 Cloud-based aggregated GPR: c GPR(ĖĀµ Z |D[i](t), ĖĻ 2 Z |D[i](t)), Algorithm 4 Agent-based fused GPR: f GPR(ĖĀµ Z |D[i](t), ĖĻ 2 Z |D[i](t), ĖĀµZ |D(t), ĖĻ2 Z |D(t)) |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] We provide the simulation details in Section 4. We also include the code in the supplementary document. |
| Open Datasets | Yes | Experiments on a synthetic dataset and two real-world datasets are conducted to demonstrate that the Byzantine-tolerant GPR algorithm is resilient to Byzantine attacks. The first dataset is collected from a seven degrees-of-freedom SARCOS anthropomorphic robot arm [13]. The second dataset Kin40k [27] is created using a robot arm simulator. |
| Dataset Splits | No | The paper mentions 'training points' and 'test points' but does not explicitly describe a separate 'validation' set or its split. |
| Hardware Specification | Yes | We conduct the experiments on a computer with Intel i7-6600 CPU, 2.60GHz and 12 GB RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for libraries, frameworks, or specialized packages used in the experiments. |
| Experiment Setup | Yes | We generate ns = 10^3, 5 Ć 10^3, 10^4, 5 Ć 10^4 training points in [0, 1], respectively, and choose nt = 120 test points randomly in [0, 1]. There are n = 40 agents in a network... We use the following squared-exponential kernel k(z, z') = Ļ^2_f exp(ā1/(2ā^2) (z ā z')^2)... we let Ī± = 2.5%, 5%, 7.5%, 10%, 12.5%, 15%. We randomly choose the agents in the network to be compromised by same-value attacks [26], and let Ī² = Ī±. Specifically, for each test point zā, the Byzantine agents only change the local predictive means to 100, that is, ĖĀµāz|D[i](t) = 100 for all t, and send this incorrect prediction to the cloud. |