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..
Fairness in Federated Learning via Core-Stability
Authors: Bhaskar Ray Chaudhury, Linyi Li, Mintong Kang, Bo Li, Ruta Mehta
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically validate our analysis on two real-world datasets, and we show that Core Fed achieves higher core-stable fairness than Fed Avg while maintaining similar accuracy. We evaluate our fair ML method Core Fed and baseline Fed Avg [21] on three datasets (Adult, MNIST and CIFAR-10) on linear model and deep neural networks. |
| Researcher Affiliation | Academia | Bhaskar Ray Chaudhury Linyi Li Mintong Kang Bo Li Ruta Mehta University of Illinois at Urbana Champaign |
| Pseudocode | Yes | We call our Algorithm as Core Fed (Fully outlined in Algorithm 1 in the appendix). |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | We evaluate our algorithm Core Fed on Adult [2], MNIST [17] and CIFAR-10 [16] datasets. |
| Dataset Splits | No | The paper mentions data construction for non-IID settings but does not explicitly provide specific percentages, sample counts, or methodology for training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments are conducted on a 1080 Ti GPU. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For Adult dataset, the utitility is selected as M ℓlog where ℓlog is the logistic loss. For CIFAR-10 and MNIST, we use cross entropy loss ℓce as the training loss with utility U becomes M ℓce. M is set to be 3.0, 1.0 and 3.0 for Adult, MNIST, and CIFAR-10, respectively, based on statistical analysis during training. We use a CNN, which has two 5x5 convolution layers followed by 2x2 max pooling and two fully connected layer with Re LU activation for MNIST and CIFAR-10. |