EasyFL
by EasyFL-AI
An easy-to-use federated learning platform
AI summary
Federated learning platform
An easy-to-use platform for federated learning on PyTorch
- stars
- 9
- forks
- 1
- watching
- 0
Similar projects
Found by comparing what the projects do, not just their names.
Federated Learning Platform
A comprehensive platform for federated learning, providing an event-driven architecture and flexible customization for various tasks in academia and industry.
Federated Learning Framework
A framework for federated representation learning with domain awareness in multi-model scenarios.
Federated Learning Framework
A framework for federated learning with partial model personalization
Federated Learning Framework
Evaluates various methods for federated learning on different models and tasks.
FL framework
A PyTorch-based framework for Federated Learning experiments
Federated learning platform
A decentralized federated learning framework based on blockchain and PyTorch.
FL platform
A federated learning platform with tools and datasets for scalable and extensible machine learning experimentation
Machine learning framework
A framework for distributed machine learning
Federated learning framework
An implementation of Fair and Consistent Federated Learning using Python.
Federated Learning Toolkit
Provides tools and APIs for designing, scheduling, and running federated machine learning jobs in a secure and efficient manner.
Federated Learning framework
An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources.
Federated Learning Framework
Develops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks.
Federated learning library
An implementation of federated learning with prototype-based methods across heterogeneous clients
Federated Learning Framework
A framework for non-IID federated learning via neural propagation
Federated Learning Framework
A flexible framework for distributed machine learning where participants train local models and collaboratively optimize them without sharing data