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].
Privacy Attacks in Decentralized Learning
Authors: Abdellah El Mrini, Edwige Cyffers, Aurélien Bellet
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate the performance of our attacks on synthetic and real graphs and datasets. Our attacks prove to be effective in practice across various graph structures. |
| Researcher Affiliation | Academia | 1School of Computer and Communication Sciences, EPFL, Switzerland 2Universit e de Lille, Inria, CNRS, Centrale Lille, UMR 9189 CRISt AL, F-59000 Lille, France 3Inria, Univ Montpellier, Montpellier, France. |
| Pseudocode | Yes | Algorithm 1 Knowledge matrix construction; Algorithm 2 Building the knowledge matrix for D-GD; Algorithm 3 Removing the attackers contributions; Algorithm 4 Building the covariance matrix |
| Open Source Code | Yes | Our code is available at https://github.com/Abdellah Elmrini/dec Attack. |
| Open Datasets | Yes | We first focus on the Cifar10 dataset (Krizhevsky, 2009); We use a small convolutional neural network (see details in Appendix G) on the MNIST dataset |
| Dataset Splits | No | The paper mentions using CIFAR10 and MNIST datasets but does not provide specific details about the training, validation, or test splits (e.g., percentages, sample counts, or explicit references to standard splits). |
| Hardware Specification | No | Experiments presented in this paper were carried out using the Grid 5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). |
| Software Dependencies | No | The paper mentions using a "fully connected layer with a softmax activation" and a "small convolutional neural network" but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We start running our attack when the model is close to convergence so that gradients are more stable. [...] The learning rate plays an important role: it should be small enough to ensure that gradients do not vary wildly across iterations. We illustrate this behavior in Appendix H. [...] logistic regression, learning rate 10^-4; [...] convnet, learning rate 10^-6 |