WwZzz/easyFL
An experimental platform for federated learning.
This tool helps machine learning researchers and data scientists run experiments with Federated Learning (FL). It allows you to define a FL task using various datasets (like image classification or natural language processing) and algorithms, then simulate and evaluate how well your models perform when data is kept separate across many devices or organizations. The output includes performance metrics and visualizations to understand model behavior.
625 stars. No commits in the last 6 months.
Use this if you are a researcher or practitioner exploring different federated learning algorithms and scenarios without needing to build the entire experimental infrastructure from scratch.
Not ideal if you are looking for a production-ready federated learning system to deploy directly into a real-world application.
Stars
625
Forks
102
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jun 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/WwZzz/easyFL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
flwrlabs/flower
Flower: A Friendly Federated AI Framework
JonasGeiping/breaching
Breaching privacy in federated learning scenarios for vision and text
anupamkliv/FedERA
FedERA is a modular and fully customizable open-source FL framework, aiming to address these...
zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on...
p2pfl/p2pfl
P2PFL is a decentralized federated learning library that enables federated learning on...