ICML2023_FeDXL
Federated learner
An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization.
Official implementation of ICML 2023 paper "FeDXL: Provable Federated Learning for Deep X-Risk Optimization".
2 stars
1 watching
0 forks
Language: Python
last commit: over 3 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
 hongliny/fco-icml21 | This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning | 12 |
 litian96/fedprox | An optimization framework designed to address heterogeneity in federated learning across distributed networks | 655 |
 illidanlab/splitmix | An algorithm for distributed learning with flexible model customization during training and testing | 40 |
 hui-po-wang/progfed | An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 |
 lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
| hongliny/fedac-neurips20 | Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
 unc-optimization/feddr | An implementation of algorithms for nonconvex federated learning optimization | 8 |
 mingruiliu-ml-lab/episode | An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |
 zackzikaixiao/fedgrab | A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. | 14 |
 lipingyi/qsfl | An optimization framework for federated learning | 12 |
 yamingguo98/fediir | An implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships | 10 |
 pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
 ignavierng/notears-admm | An implementation of Bayesian network structure learning with continuous optimization for federated learning. | 10 |
 divyansh03/fedexp | An implementation of a federated averaging algorithm with an extrapolation approach to speed up distributed machine learning training on client-held data. | 9 |
 baowenxuan/fedcollab | An algorithm that optimizes collaboration in federated learning by clustering clients into non-overlapping coalitions based on data quantity and pairwise distribution distances. | 16 |