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 [1].
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
| 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. |