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..
Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation
Authors: Heng Zhu, Qing Ling
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments to verify the effectiveness of DP-RSA on MNIST and CIFAR10 datasets, respectively. |
| Researcher Affiliation | Academia | 1Sun Yat-sen University 2University of California, San Diego |
| Pseudocode | Yes | Algorithm 1 DP-RSA |
| Open Source Code | Yes | The code is available at https://github.com/oyhah/DP-RSA |
| Open Datasets | Yes | For MNIST, we train a two-layer neural network... For CIFAR10, we train a convolutional neural network (CNN) model... |
| Dataset Splits | No | The paper describes training sample distribution but does not specify a separate validation dataset or its size/percentage for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python 3.x, PyTorch x.y). |
| Experiment Setup | Yes | The regularization term is f0( x) = 0.002 x 2. The penalty parameter λ is set to 0.01 and the step size αt is set to be constant as α = 0.01. [...] The privacy loss ϵ is set to 0.2, 0.4 and 1.38. |