Byzantine-tolerant federated Gaussian process regression for streaming data
Authors: Xu Zhang, Zhenyuan Yuan, Minghui Zhu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 xxz313@psu.edu Zhenyuan Yuan Pennsylvania State University zqy5086@psu.edu Minghui Zhu Pennsylvania State University muz16@psu.edu |
| 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(ˇµ Z |D[i](t), ˇσ 2 Z |D[i](t)), Algorithm 4 Agent-based fused GPR: f GPR(ˇµ Z |D[i](t), ˇσ 2 Z |D[i](t), ˆµZ |D(t), ˆσ2 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 × 10^3, 10^4, 5 × 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') = σ^2_f exp(−1/(2ℓ^2) (z − z')^2)... we let α = 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 β = α. Specifically, for each test point z∗, the Byzantine agents only change the local predictive means to 100, that is, ˇµ∗z|D[i](t) = 100 for all t, and send this incorrect prediction to the cloud. |