divfl
Client selection method
Proposes a method for selecting a diverse subset of clients in federated learning to improve convergence and fairness
Diverse Client Selection for Federated Learning via Submodular Maximization
29 stars
4 watching
12 forks
Language: Python
last commit: over 2 years ago Related projects:
Repository | Description | Stars |
---|---|---|
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 |
symbioticlab/oort | This repository provides scripts and instructions for reproducing experiments on efficient federated learning via guided participant selection | 124 |
litian96/ditto | A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. | 137 |
optimization-ai/icml2023_fedxl | An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
xiyuanyang45/dynamicpfl | A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 51 |
diaoenmao/semifl-semi-supervised-federated-learning-for-unlabeled-clients-with-alternate-training | An implementation of semi-supervised federated learning for improving the performance of a server using distributed clients with unlabeled data | 34 |
pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 54 |
mingruiliu-ml-lab/episode_plusplus | An algorithm for Federated Learning that handles client subsampling and data heterogeneity with unbounded smoothness | 0 |
federatedai/fate-client | Provides tools and APIs for designing, scheduling, and running federated machine learning jobs in a secure and efficient manner. | 3 |
lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
l3030/delta_fl | An implementation of an unbiased Federated Learning sampling scheme designed to improve model convergence and reduce variance in client participation. | 5 |
mmorafah/pacfl | Implementation of federated learning algorithms for distributed machine learning on private client data | 37 |
lipingyi/qsfl | An optimization framework for federated learning | 11 |
lyn1874/fedpvr | An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
mingruiliu-ml-lab/episode | An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |