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..
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
Authors: Syed Irfan Ali Meerza, Jian Liu
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three datasets demonstrate the effectiveness and efficiency of our attack, even with state-of-the-art fairness optimization algorithms and secure aggregation rules employed. |
| Researcher Affiliation | Academia | Syed Irfan Ali Meerza and Jian Liu University of Tennessee, Knoxville EMAIL, EMAIL, |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/irfan Mee/EAB-FL |
| Open Datasets | Yes | We evaluate the proposed EAB-FL using the following three datasets in non-IID settings: (1) Celeb A [Liu et al., 2018]: (2) Adult Income [Dua and Graff, 2017]: (3) UTK Faces [Zhang et al., 2017]: To show the real-world implications, we also apply EAB-FL to the Movie Lens 1M dataset [Harper and Konstan, 2015] |
| Dataset Splits | No | The server can evaluate the accuracy of the submitted model updates on a validation set. The paper mentions the use of a validation set but does not provide specific details on how the dataset was split into training, validation, and testing subsets, such as percentages or sample counts. |
| Hardware Specification | Yes | Table 3 shows the average time required to successfully attack the global model per communication round on the Celeb A dataset using an Nvidia Quadro A100 GPU. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or other libraries). |
| Experiment Setup | No | The paper describes the attack's optimization problem with parameters like Îł and Ï (Equation 7) and Îș for biasing dataset selection. However, it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings in the main text. |