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..
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts
Authors: Kun Jin, Tongxin Yin, Zhongzhu Chen, Zeyu Sun, Xueru Zhang, Yang Liu, Mingyan Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results validate our analysis and provide valuable insights into real-world applications. We show empirically that P-Fed Avg also has good performance on FMNIST (Deng 2012) and Cifar-10. |
| Researcher Affiliation | Academia | 1 University of Michigan 2 The Ohio State University 3 University of California, Santa Cruz |
| Pseudocode | No | The paper describes the P-Fed Avg algorithm in Section 2.4, but it does not include a formally structured 'Algorithm' or 'Pseudocode' block. |
| Open Source Code | Yes | Our code is publicly accessible1. 1https://github.com/tsy19/Performative Fed Avg |
| Open Datasets | Yes | Demonstrating the efficacy of P-Fed Avg on the Kaggle dataset2 as per Perdomo et al. (2020). We show empirically that P-Fed Avg also has good performance on FMNIST (Deng 2012) and Cifar-10. |
| Dataset Splits | No | The paper mentions 'a 10% subset of the training data' for each client in the Credit Score Strategic Classification, but does not provide specific train/validation/test split percentages or absolute counts for any of the datasets used (Kaggle, FMNIST, Cifar-10). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | For the Credit Score Strategic Classification experiment, the paper states using 'partial participation with K = 5', '5 gradient descent steps per round', and 'a minibatch of size 4'. For Performative Image Classification it states 'β = 0.5'. |