ByMI: Byzantine Machine Identification with False Discovery Rate Control
Authors: Chengde Qian, Mengyuan Wang, Haojie Ren, Changliang Zou
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on both synthetic and real data validate the theoretical results and demonstrate the effectiveness of our proposed method on Byzantine machine identification. |
| Researcher Affiliation | Academia | 1School of Statistics and Data Sciences, LPMC, KLMDASR and LEBPS, Nankai University, Tianjin, China 2School of Mathematical Sciences, Shanghai Jiao Tong University. |
| Pseudocode | Yes | C. Pseudocode of By MI |
| Open Source Code | Yes | The code is available at https://github.com/mywang99/By MI. |
| Open Datasets | Yes | Datasets. The MNIST (Le Cun et al., 1998), Fashion MNIST (Xiao et al., 2017) and CIFAR10 (Krizhevsky, 2009) datasets are used to verify the performance of our By MI. |
| Dataset Splits | No | For MNIST and F-MNIST, all of the samples in the training set are randomly divided into m + 1 = 150 machines (including one master machine) with an equal sample size n = 400. For CIFAR10, we fix m + 1 = 125 and n = 400. |
| Hardware Specification | Yes | All the experiments are conducted on an Ubuntu 20.04 LTS server with 64 Intel(R) Xeon(R) Gold 5218 CPUs @ 2.30GHz, 128G RAM and the R platform with version 4.0.2. |
| Software Dependencies | Yes | All the experiments are conducted on an Ubuntu 20.04 LTS server with 64 Intel(R) Xeon(R) Gold 5218 CPUs @ 2.30GHz, 128G RAM and the R platform with version 4.0.2. |
| Experiment Setup | Yes | The target FDR level is fixed as α = 0.1. Specifically, for the OOD attack we replace the covariates xi s on Byzantine machines by xi = 0.7xi + εp where εp is from Np(νp, σ2Ip) with νp Rp randomly sampled from the standard multivariate normal distribution and σ = 0.2. For the IPM attack, the Byzantine gradients are assigned as a g, where g = 1 |G| P |