H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets
Authors: Guanqiang Zhou, Ping Xu, Yue Wang, Zhi Tian
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the performance of our algorithm for a classification task using the logistic regression model on the Spambase dataset. |
| Researcher Affiliation | Academia | Guanqiang Zhou George Mason University gzhou4@gmu.edu Ping Xu University of Texas Rio Grande Valley ping.t.xu@utrgv.edu Yue Wang Georgia State University ywang182@gsu.edu Zhi Tian George Mason University ztian1@gmu.edu |
| Pseudocode | Yes | Algorithm 1 Norm-Based Screening, Algorithm 2 H-nobs: Fair & Byzantine-Robust Distributed Gradient Descent |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for its proposed methodology or a link to a code repository. |
| Open Datasets | Yes | using the logistic regression model on the Spambase dataset [21]. We assign 2/3 of the 4601 total samples for training and the other 1/3 for testing. The results for these two datasets are presented in Appendix E. Table 3: The performance of H-nobs on the Law School dataset (with no attack). Table 4: The performance of H-nobs on the Credit Card Client dataset (with no attack). |
| Dataset Splits | No | We assign 2/3 of the 4601 total samples for training and the other 1/3 for testing. The paper explicitly mentions training and testing splits, but does not specify a separate validation split or its size/proportion. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper mentions using a 'logistic regression model' but does not specify any software libraries or their version numbers, such as Python, PyTorch, TensorFlow, or scikit-learn. |
| Experiment Setup | Yes | In Figure 1, we plot the performance curves of model accuracy for the five considered aggregation measures... using learning rate η = 1 and number of iterations T = 300. In Table 1, we document both the model accuracy and the variance of local accuracies (the metric of model fairness) using different values of q and screening percentage β. The learning rate η and number of iterations T for each q value are carefully selected to ensure fast and stable convergence. Finally, we compare H-nobs... using learning rate η = 0.5 and number of iterations T = 1000. |