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..
Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization
Authors: Aniket Murhekar, Zhuowen Yuan, Bhaskar Ray Chaudhury, Bo Li, Ruta Mehta
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical validation on MNIST and CIFAR-10 substantiates our theoretical analysis. |
| Researcher Affiliation | Academia | Aniket Murhekar1 Zhuowen Yuan1 Bhaskar Ray Chaudhury1 Bo Li1 Ruta Mehta1 1University of Illinois, Urbana-Champaign |
| Pseudocode | Yes | We present the full description of the algorithm for Fed BR-BG and Fed BR as Algorithm 1 and Algorithm 2 in Appendix C, respectively. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | We perform the evaluation on the MNIST (Le Cun et al. [2010]) and CIFAR-10 (Krizhevsky [2009]) datasets. |
| Dataset Splits | No | The paper states 'each agent has 100 training images and 10 testing images' but does not specify a validation split or how validation was handled. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. It mentions model architectures (CNN, VGG11) but not the underlying computational resources. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set global learning rate η to 1.0, local learning rate α to 0.01, and momentum to 0.9. We set the number of contribution updating steps to 100 and the sample number interval to 10. |