multidistributionlearning
On-demand sampling algorithm
This project provides an implementation of On-Demand Sampling: Learning Optimally from Multiple Distributions, a method for learning from multiple distributions in federated learning.
Official implementation of On-Demand Sampling: Learning Optimally from Multiple Distributions (Neurips 2022)
8 stars
4 watching
1 forks
Language: Python
last commit: over 2 years ago federated-learninggdrolearning-theorymin-max-optimization
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 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
 mingruiliu-ml-lab/episode_plusplus | An algorithm for Federated Learning that handles client subsampling and data heterogeneity with unbounded smoothness | 0 |
 optimization-ai/icml2023_fedxl | An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
 lx10077/fedavgpy | The purpose of this project is to investigate the convergence of a federated learning algorithm on non-IID (non-identically and independently distributed) data. | 255 |
 wwzzz/fedgs | An implementation of a federated learning approach using graph-based sampling to handle arbitrary client availability in distributed machine learning | 16 |
 unc-optimization/feddr | An implementation of algorithms for nonconvex federated learning optimization | 8 |
 hongliny/fedac-neurips20 | Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
 mc-nya/fednest | An implementation of a federated optimization algorithm for distributed machine learning | 6 |
 xidongwu/federated-minimax-and-conditional-stochastic-optimization | This project presents optimization techniques for federated learning and minimax games in the context of machine learning | 1 |
| mingruiliu-ml-lab/episode | An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |
 gaoliang13/feddc | Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift | 81 |
 ignavierng/notears-admm | An implementation of Bayesian network structure learning with continuous optimization for federated learning. | 10 |
 sungwon-han/fedx | An unsupervised federated learning algorithm that uses cross knowledge distillation to learn meaningful data representations from local and global levels. | 69 |
 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 |