ifca
Clustered Federated Learning Framework
A framework for decentralized collaborative learning across multiple clusters with efficient communication and data management strategies.
Codebase for An Efficient Framework for Clustered Federated Learning.
106 stars
2 watching
26 forks
Language: Python
last commit: over 4 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A framework for collaborative distributed machine learning in enterprise environments. | 500 |
| An implementation of a federated learning method to optimize multiple models simultaneously while maintaining user privacy. | 160 |
| An implementation of Fair and Consistent Federated Learning using Python. | 20 |
| A framework for non-IID federated learning via neural propagation | 6 |
| A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
| A framework for personalized federated learning to balance fairness and robustness in decentralized machine learning systems. | 138 |
| Implementation of various federated learning algorithms to mitigate dimensional collapse in heterogeneous federated learning environments | 64 |
| Evaluates various methods for federated learning on different models and tasks. | 19 |
| A Python framework for collaborative machine learning without sharing sensitive data | 738 |
| This project enables federated learning across partially class-disjoint data with curated bilateral curation. | 11 |
| A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. | 25 |
| Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |
| 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 |
| A framework for federated representation learning with domain awareness in multi-model scenarios. | 2 |