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..
Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning
Authors: Junyuan Hong, Haotao Wang, Zhangyang Wang, Jiayu Zhou
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the rationality and effectiveness of our method through extensive experiments. Especially, the proposed method is shown to grant federated models remarkable robustness even when only a small portion of users afford AT during learning. Source code can be accessed at https://github.com/illidanlab/Fed RBN. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Michigan State University 2Department of Computer Science and Engineering, University of Texas at Austin |
| Pseudocode | Yes | Algorithm 1: Fed RBN: user-end training Algorithm 2: Fed RBN: server-end training |
| Open Source Code | Yes | Source code can be accessed at https://github.com/illidanlab/Fed RBN. |
| Open Datasets | Yes | We used two multi-domain datasets for the setting. The first is a subset (30%) of DIGITS, a benchmark for domain adaption (Peng et al. 2019b). DIGITS includes 5 different domains: MNIST (MM) (Lecun et al. 1998), SVHN (SV) (Netzer et al. 2011), USPS (US) (Hull 1994), Synth Digits (SY) (Ganin and Lempitsky 2015), and MNIST-M (MM) (Ganin and Lempitsky 2015). The second dataset is DOMAINNET (Peng et al. 2019a) processed by (Li et al. 2020b). |
| Dataset Splits | No | We uniformly split the dataset for each domain into 10 subsets for DIGITS and 5 for DOMAINNET, following (Li et al. 2020b), which are distributed to different users, respectively. ... For AT users, we use n-step PGD (projected gradient descent) attack (Madry et al. 2018) with a constant noise magnitude ϵ. Following (Madry et al. 2018), we use ϵ = 8/255, n = 7, and attack inner-loop step size 2/255, for training, validation, and test. |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU model, CPU type, memory size) used for running the experiments. It only refers to 'resource constraints' and 'computation capacities' generally. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as Python, PyTorch, TensorFlow, or other libraries. |
| Experiment Setup | Yes | For AT users, we use n-step PGD (projected gradient descent) attack (Madry et al. 2018) with a constant noise magnitude ϵ. Following (Madry et al. 2018), we use ϵ = 8/255, n = 7, and attack inner-loop step size 2/255, for training, validation, and test. We uniformly split the dataset for each domain into 10 subsets for DIGITS and 5 for DOMAINNET... Each user trains local model for one epoch per communication round. |