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..
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Authors: Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Practically, we conduct comprehensive experiments across a range of federated datasets, and provide the first benchmark for certified robustness against backdoor attacks in federated learning. |
| Researcher Affiliation | Collaboration | 1University of Illinois at Urbana-Champaign 2Zhejiang University 3IBM Research. Correspondence to: Chulin Xie <EMAIL>, Pin-Yu Chen <EMAIL>, Bo Li <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Federated averaging with parameters clipping and perturbing; Algorithm 2 Certification of parameters smoothing |
| Open Source Code | Yes | Our code is publicaly available at https://github.com/AI-secure/CRFL. |
| Open Datasets | Yes | We train the FL system following our CRFL framework with three datasets: Lending Club Loan Data (LOAN) (Kan, 2019), MNIST (Le Cun & Cortes, 2010), and EMNIST (Cohen et al., 2017). |
| Dataset Splits | No | The paper mentions 'training data' and 'test sets' but does not explicitly provide percentages, sample counts, or specific predefined splits for training, validation, and testing datasets to reproduce the experiment. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch, TensorFlow, scikit-learn). |
| Experiment Setup | Yes | In all experiments, unless otherwise stated, we use σT = 0.01 to generate M = 1000 noisy models in parameter smoothing procedure, and use the error tolerance α = 0.001. |