FedEM
Federated learning framework
Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.
Official code for "Federated Multi-Task Learning under a Mixture of Distributions" (NeurIPS'21)
157 stars
3 watching
28 forks
Language: Python
last commit: almost 4 years agodeep-learningfederated-learningmachine-learningpersonalized-federated-learningpytorch
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A federated learning framework with personalized memorization using deep neural networks and k-nearest neighbors for collaborative learning of statistical models | 43 | |
| A framework for collaborative distributed machine learning in enterprise environments. | 500 | |
| A project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models | 9 | |
| A flexible framework for distributed machine learning where participants train local models and collaboratively optimize them without sharing data | 743 | |
| A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. | 25 | |
| Enables the training and validation of machine learning models on distributed datasets in a secure and scalable manner. | 274 | |
| A framework for federated representation learning with domain awareness in multi-model scenarios. | 2 | |
| An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 | |
| An implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation. | 291 | |
| Personalized Subgraph Federated Learning framework for distributed machine learning | 45 | |
| An open source federated learning framework designed to be secure, scalable and easy-to-use for enterprise environments | 145 | |
| An implementation of a heterogenous federated learning framework using model distillation. | 150 | |
| An open-source project exploring Federated Learning model updates and their rank structure using data from various datasets. | 14 | |
| Evaluates various methods for federated learning on different models and tasks. | 19 | |
| This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |