FedWeIT
Federated learning framework
An implementation of Federated Continual Learning with Weighted Inter-client Transfer using TensorFlow 2.
This is an official Tensorflow-2 implementation of Federated Continual Learning with Inter-Client Weighted Transfer
102 stars
2 watching
27 forks
Language: Python
last commit: over 3 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| wyjeong/fedmatch | A project implementing Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | 80 |
 wenkehuang/fccl | A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
 fangxiuwen/robust_fl | An implementation of a robust federated learning framework for handling noisy and heterogeneous clients in machine learning. | 43 |
 kai-yue/ntk-fed | A framework for federated learning that leverages the neural tangent kernel to address statistical heterogeneity in distributed machine learning. | 3 |
 yutong-dai/fednh | An implementation of a federated learning framework for handling data heterogeneity in decentralized settings | 38 |
 jiayunz/fedalign | Develops an alignment framework for federated learning with non-identical client class sets | 4 |
 xtra-computing/fedov | Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |
 scaleoutsystems/fedn | An open source federated learning framework designed to be secure, scalable and easy-to-use for enterprise environments | 145 |
 hypervoyager/pfl | An implementation of heterogeneous federated learning with parallel edge and server computation | 17 |
| xtra-computing/fedsim | A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. | 25 |
 ibm/federated-learning-lib | A framework for collaborative distributed machine learning in enterprise environments. | 500 |
 aiot-mlsys-lab/fedrolex | An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 |
 hongyouc/fed-rod | Develops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks. | 14 |
 mediabrain-sjtu/fedgela | This project enables federated learning across partially class-disjoint data with curated bilateral curation. | 11 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |