Federated Learning with Convex Global and Local Constraints
Authors: Chuan He, Le Peng, Ju Sun
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments show the effectiveness of our algorithm in performing Neyman-Pearson classification and fairness-aware learning with nonconvex constraints, in an FL setting. |
| Researcher Affiliation | Academia | Chuan He EMAIL Department of Computer Science and Engineering, University of Minnesota Le Peng EMAIL Department of Computer Science and Engineering, University of Minnesota Ju Sun EMAIL Department of Computer Science and Engineering, University of Minnesota |
| Pseudocode | Yes | Algorithm 1 A proximal AL based FL algorithm for solving Eq. (5) Algorithm 2 An inexact ADMM based FL algorithm for solving Eq. (16) |
| Open Source Code | Yes | The code to implement the proposed algorithm on these numerical examples is available at https://github.com/PL97/Constr_FL. |
| Open Datasets | Yes | We consider three real-world datasets, namely breast-cancer-wisc , adult-a , and monks-1 , from the UCI repository2 and described in Appendix E.1. For the real-world dataset, we consider adult-b 3: each sample in this dataset has 39 features and one binary label. This dataset can be found in https://github.com/heyaudace/ml-bias-fairness/tree/master/data/adult. |
| Dataset Splits | Yes | To simulate the FL setting, we divide each dataset into n folds, mimicking local clients, each holding the same amount of data with equal ratios of the two classes. To simulate the FL setting, we divide the 22, 654 training samples from the adult-b dataset into n folds and distribute them to n local clients. The central server holds the 5, 659 test samples from the adultb dataset. |
| Hardware Specification | Yes | All experiments are carried out on a Windows system with an AMD EPYC 7763 64-core processor |
| Software Dependencies | No | All experiments are carried out on a Windows system with an AMD EPYC 7763 64-core processor, and all algorithms are implemented in Python. The paper mentions Python but does not specify a version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | We set the other parameters for Algorithm 1 and c Prox-AL as µ0 i = (0, . . . , 0)T 0 i n, s = 0.001 and β = 300. We also set ρi = 0.01 1 i n for Algorithm 2. We set the other parameters for Algorithm 1 and c Prox-AL as µ0 i = (0, . . . , 0)T 0 i n, s = 0.001 and β = 10. We also set ρi = 108 1 i n for Algorithm 2. |