FedDC
Federated Learning Engine
An implementation of federated daisy-chaining and model averaging for distributed machine learning
8 stars
1 watching
3 forks
Language: Jupyter Notebook
last commit: 12 months ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 gaoliang13/feddc | Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift | 81 |
 zhenqincn/fedapen | An implementation of cross-silo federated learning with adaptability to statistical heterogeneity | 12 |
 jinheonbaek/fed-pub | Personalized Subgraph Federated Learning framework for distributed machine learning | 45 |
 shams-sam/fedoptim | An open-source project exploring Federated Learning model updates and their rank structure using data from various datasets. | 14 |
 umd-huang-lab/swift | An open-source framework for decentralized federated learning with wait-free model communication | 10 |
 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 |
 wyjeong/fedmatch | A project implementing Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | 80 |
 lkyddd/gradma | A framework for accelerating federated learning with memory-based acceleration and alleviation of catastrophic forgetting | 13 |
 alshedivat/fedpa | A modular JAX implementation of federated learning via posterior averaging for decentralized optimization | 50 |
 diogenes0319/fedmd_clean | An implementation of a heterogenous federated learning framework using model distillation. | 150 |
 tsingz0/fedala | An implementation of a federated learning method for personalized models on non-iid datasets. | 116 |
 liruichenspace/fedfusion | An implementation of federated learning with data-agnostic distribution fusion using PyTorch. | 8 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
 wenkehuang/fccl | A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
 lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |