Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks
Authors: Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 46,287 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,221 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, using our equivalence, we derive a practical attack that we show (theoretically and empirically) can be very effective against classical personalized federated learning models. Our experiment also shows the effectiveness of a simple protection, which prevents attackers from arbitrarily manipulating the trained algorithm. |
| Researcher Affiliation | Academia | IC Schoold, EPFL, Lausanne, Switzerland. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The code can be found at https://github.com/ LPD-EPFL/Attack_Equivalence. |
| Open Datasets | Yes | We deployed CGA to bias the federated learning of MNIST. We constructed a setting where 10 idle users draw randomly 10 data points from the Fashion MNIST dataset. We considered VGG 13-BN, which was pretrained on cifar-10 by (Phan, 2021). |
| Dataset Splits | No | The paper mentions 'training set' and 'test dataset' but does not specify validation splits or numerical proportions for train/val/test splits. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) were found. |
| Software Dependencies | No | The paper mentions 'Pytorch' in a citation (Phan, 2021) related to a pretrained model, but does not provide specific version numbers for the software dependencies of its own implementation. |
| Experiment Setup | Yes | We use λ = 1, Adam optimizer and a decreasing learning rate. |