nclabteam/FedEasy
FedEasy is an intuitive powerful yet simple to use Federated Learning framework. Our goal is to streamline the process of setting up and running federated learning experiments with ease, making advanced machine learning techniques accessible to researchers and developers alike.
This framework helps machine learning researchers and developers easily set up and run federated learning experiments. It takes various datasets (like MNIST, CIFAR10) and model architectures as input, enabling collaborative model training across decentralized data sources. The output is a trained federated learning model and evaluation results, ideal for those exploring privacy-preserving AI.
Use this if you need to quickly configure and execute federated learning simulations for research or development without deep expertise in distributed systems.
Not ideal if you are a business user looking for a no-code solution to deploy federated learning in a production environment, as it requires coding and setup.
Stars
13
Forks
8
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 25, 2026
Commits (30d)
0
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