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 [1].
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. |