orchestra
Federated learner
An open-source software framework for unsupervised federated learning via clustering and self-supervised learning.
Source code for the ICML 2022 paper: "Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering"
54 stars
3 watching
10 forks
Language: Python
last commit: over 3 years agoRelated 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 |
 hongliny/fedac-neurips20 | Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
 ignavierng/notears-admm | An implementation of Bayesian network structure learning with continuous optimization for federated learning. | 10 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
 yamingguo98/fediir | An implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships | 10 |
 tsingz0/fedala | An implementation of a federated learning method for personalized models on non-iid datasets. | 116 |
 xtra-computing/fedov | Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |
 optimization-ai/icml2023_fedxl | An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
 lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
 mingruiliu-ml-lab/episode | An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |
 federatedai/fate-client | Provides tools and APIs for designing, scheduling, and running federated machine learning jobs in a secure and efficient manner. | 3 |
 lunanbit/fedul | This project presents an approach to federated learning that leverages unsupervised techniques to adapt models to unlabeled data without requiring labels. | 33 |
 aiot-mlsys-lab/fedrolex | An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 |
 ibm/federated-learning-lib | A framework for collaborative distributed machine learning in enterprise environments. | 500 |
 atuannguyen/fedsr | An implementation of a domain generalization method for federated learning using Python and PyTorch | 26 |