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 | Conference PDF | Archive PDF | Plain Text | 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'. |