fair_flearn
Fair learning algorithm
This project develops and evaluates algorithms for fair resource allocation in federated learning, aiming to promote more inclusive AI systems.
Fair Resource Allocation in Federated Learning (ICLR '20)
244 stars
7 watching
60 forks
Language: Python
last commit: about 1 year ago alpha-fairnessdistributed-optimizationfairness-mlfederated-learning
Related projects:
| Repository | Description | Stars |
|---|---|---|
 fairlearn/fairlearn | A tool to assess and mitigate unfairness in AI systems, helping developers ensure their models do not disproportionately harm certain groups of people. | 1,974 |
 taoqi98/fairvfl | A collection of code implementing the FairVFL algorithm and its associated data structures and utilities for efficient and accurate fairness-aware machine learning model training. | 7 |
 lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
 harliwu/fedamd | This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
 zjelveh/learning-fair-representations | An implementation of Zemel et al.'s 2013 algorithm for learning fair representations in machine learning | 26 |
 mbilalzafar/fair-classification | Provides a Python implementation of fairness mechanisms in classification models to mitigate disparate impact and mistreatment. | 190 |
| litian96/ditto | A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. | 138 |
 ignavierng/notears-admm | An implementation of Bayesian network structure learning with continuous optimization for federated learning. | 10 |
 optimization-ai/icml2023_fedxl | An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
 pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
 lunanbit/fedul | This project presents an approach to federated learning that leverages unsupervised techniques to adapt models to unlabeled data without requiring labels. | 33 |
 lyn1874/fedpvr | An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
 zfancy/sfat | Combating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation | 28 |
 diaoenmao/heterofl-computation-and-communication-efficient-federated-learning-for-heterogeneous-clients | An implementation of efficient federated learning algorithms for heterogeneous clients | 155 |
 easyfl-ai/easyfl | An easy-to-use platform for federated learning on PyTorch | 9 |