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..
Fair Federated Learning via the Proportional Veto Core
Authors: Bhaskar Ray Chaudhury, Aniket Murhekar, Zhuowen Yuan, Bo Li, Ruta Mehta, Ariel D. Procaccia
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate that Rank-Core Fed outperforms baselines in terms of fairness on different datasets. |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign 2University of Chicago 3Harvard University. |
| Pseudocode | Yes | Algorithm 1 Computes a representative set of P; Algorithm 2 Rank-Core-Fed: Finds a θ that belongs to the proportional veto core of P |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our algorithm Rank-Core-Fed on rotated MNIST (Le Cun et al., 2010) and CIFAR-10 (Krizhevsky et al., 2009) datasets. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits, specific percentages, or sample counts. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions the use of CNN and VGG11 models but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For MNIST, we use a CNN, which has two 5 5 convolution layers followed by two fully connected layers with Re LU activation. For CIFAR-10, we evaluate with a more complex network, VGG11 (Simonyan & Zisserman, 2014). In all our experiments, we define agent utility as M Lce, where Lce refers to the average cross entropy loss on the agent s local test data. We set M to be 1.0 in our experiments. For all baselines and our algorithm, we set the number of iterations of the global model update to 50. |