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..
Provably Secure Federated Learning against Malicious Clients
Authors: Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong6885-6893
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on MNIST and Human Activity Recognition datasets. For instance, our method can achieve a certified accuracy of 88% on MNIST when 20 out of 1,000 clients are malicious. Evaluation: We evaluate our methods on MNIST and Human Activity Recognition datasets. |
| Researcher Affiliation | Academia | Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong Duke University, Durham, NC 27708 EMAIL |
| Pseudocode | Yes | Algorithm 1 Single-global-model federated learning; Algorithm 2 Computing Predicted Label and Certified Security Level |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | We use MNIST (Le Cun, Cortes, and Burges 1998) and Human Activity Recognition (HAR) datasets (Anguita et al. 2013). |
| Dataset Splits | No | The paper mentions 60,000 training examples and 10,000 testing examples for MNIST, and for HAR, '75% of each user s examples as training examples and the rest as testing examples.' There is no explicit mention of a separate validation split percentage or count. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Fed Avg and provides hyperparameters but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Table 1: Federated learning settings and hyperparameters. provides specific values for 'global Iter', 'local Iter', 'Learning rate η', and 'Batch size'. The text also states: 'In particular, we set the global Iter in Table 1 because Fed Avg converges with such settings.' |