Effective passive membership inference attacks in federated learning against overparameterized models
Authors: Jiacheng Li, Ninghui Li, Bruno Ribeiro
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through an extensive empirical evaluation (described in Section 4) shows that, at later gradient update rounds t 1 of the optimization (in our experiments t > 50 if trained from scratch and t 2 if fine-tuned) of medium to large neural networks and at nearly any stage of the fine tuning of large pre-trained models gradient vectors of different training instances are orthogonal in the same way distinct samples of independent isotropic random vectors are orthogonal (such as two high-dimensional Gaussian random vectors with zero mean and diagonal covariance matrix (isotropic)). |
| Researcher Affiliation | Academia | Jiacheng Li, Ninghui Li & Bruno Ribeiro Department of Computer Science Purdue University West Lafayette, IN 47903, USA li2829@purdue.edu,{ninghui,ribeiro}@cs.purdue.edu |
| Pseudocode | No | The paper describes the methods textually and mathematically but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for its methodology is made available or provide a link to a code repository. |
| Open Datasets | Yes | The medical-MNIST dataset apolanco3225 (2017) is a simple MNIST-style medical images in 64x64 dimension; There were originally taken from other datasets and processed into such style. |
| Dataset Splits | Yes | We divide this dataset into 3 disjoint set: 40,000 images for training, 5,000 images for validation and 8,724 images for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not explicitly mention specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, TensorFlow 2.x). |
| Experiment Setup | Yes | A detailed description of the training parameters is given in Table 6 in the Appendix. |