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..
High-Fidelity Gradient Inversion in Distributed Learning
Authors: Zipeng Ye, Wenjian Luo, Qi Zhou, Yubo Tang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the superiority of our approach, reveal the potential vulnerabilities of the distributed learning paradigm, and emphasize the necessity of developing more secure mechanisms. |
| Researcher Affiliation | Academia | Zipeng Ye1, 2, Wenjian Luo1, 2, 3*, Qi Zhou1, 2, Yubo Tang1, 2 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 2Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies 3Peng Cheng Laboratory |
| Pseudocode | Yes | Pseudocode of this part is provided in Appendix A. |
| Open Source Code | Yes | Source code is available at https://github.com/Mi Lab-HITSZ/2023Ye HFGrad Inv. |
| Open Datasets | Yes | We conduct experiments for large-scale image classification task using Image Net ILSVRC 2012 dataset (Deng et al. 2009), as well as randomly collected images from Web. |
| Dataset Splits | Yes | We randomly select 64 images from the validation set of Image Net, and their label distributions are shown in Fig. 4 (a). |
| Hardware Specification | Yes | optimized with 15K iterations on NVIDIA TITAN RTX GPUs. |
| Software Dependencies | No | The paper mentions using PyTorch library and Adam optimizer, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The dropout rate we used in (10) is set as 0.3. We set β = 2 in (11), ˆλtv = 0.01, ˆλBN = 10 4, T1 = 3, 000, T2 = 5, 000 in scheduling strategy. We use Adam (Kingma and Ba 2014) for optimization with a step learning rate decay, and each batch is optimized with 15K iterations on NVIDIA TITAN RTX GPUs. |