SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
Authors: Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations on both tabular and image datasets show that SF-PATE not only achieves better accuracy, privacy, and fairness tradeoffs with respect to the current state of the art, but it is also significantly faster. |
| Researcher Affiliation | Academia | Cuong Tran1 , Keyu Zhu2 , Ferdinando Fioretto3 and Pascal Van Hentenryck2 1 Syracuse University 2 Georgia Institute of Technology 3 University of Virginia |
| Pseudocode | No | The paper describes algorithms (SFS-PATE, SFT-PATE) and their steps in text and mathematical equations, but it does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The evaluation is conducted using four UCI tabular datasets: Bank, Parkinson, Income and Credit Card [Blake, 1998], and UTKFace [Hwang et al., 2020], a vision dataset. |
| Dataset Splits | No | The paper mentions repeating experiments using random seeds and evaluating performance, but it does not explicitly provide specific train/validation/test dataset splits (percentages, sample counts, or detailed methodology) required for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU models, CPU types, or detailed system specifications. |
| Software Dependencies | No | The paper mentions using certain frameworks and models (e.g., Lagrangian dual scheme, Resnet 50), but it does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | To ensure a fair comparison, the experimental analysis uses the same architectures, model initialization θ, and parameters for all models (including the baselines models PF-LD and M)... A more detailed description of these approaches, the settings adopted, hyperparameters optimization, and the datasets is deferred to Appendix D. |