Provably Secure Federated Learning against Malicious Clients
Authors: Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong6885-6893
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {xiaoyu.cao, jinyuan.jia, neil.gong}@duke.edu |
| 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.' |