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..
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
Authors: kangyang Luo, Shuai Wang, Yexuan Fu, Xiang Li, Yunshi Lan, Ming Gao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments on various image classification tasks illustrate that DFRD achieves significant performance gains compared to SOTA baselines. Our code is here: https://anonymous.4open.science/r/DFRD-0C83/. |
| Researcher Affiliation | Academia | Kangyang Luo1, Shuai Wang1, Yexuan Fu1, Xiang Li1 , Yunshi Lan1, Ming Gao1,2 School of Data Science & Engineering1 KLATASDS-MOE in School of Statistics2 East China Normal University Shanghai, China |
| Pseudocode | Yes | Moreover, we present pseudocode for DFRD in Appendix C. |
| Open Source Code | Yes | Our code is here: https://anonymous.4open.science/r/DFRD-0C83/. |
| Open Datasets | Yes | In this paper, we evaluate different methods with six real-world image classification task-related datasets, namely Fashion-MNIST [69] (FMNIST in short), SVHN [70], CIFAR-10, CIFAR-100 [71], Tiny-image Net3 and Food101 [73]. We detail the six datasets in Appendix B. 3http://cs231n.stanford.edu/tiny-imagenet-200.zip |
| Dataset Splits | No | The paper mentions partitioning the training set for clients and using a test set, but does not provide specific details on train/validation splits or their percentages/counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Unless otherwise specified, all experiments are performed on a centralized network with N = 10 active clients. We set ω {0.01, 0.1, 1.0} to mimic different data heterogeneity scenarios... We fix σ = 4 and consider ρ {5, 10, 40}... Unless otherwise specified, we set βtran and βdiv both to 1 in training generator, while in robust model distillation, we set λ = 0.5 and α = 0.5. |