ChristianInterno/FedLEx
Federated Loss Exploration for Improved Convergence on Non-IID Data (IJCNN2024)
This project helps researchers working with federated learning models achieve better and more robust model performance, especially when their data is not uniformly distributed across different clients. It takes your federated learning setup, where clients contribute data for a shared model, and provides a method to create a 'global guidance matrix.' This matrix then helps clients improve their gradient updates, leading to a more accurate and stable global model. Researchers and machine learning practitioners who deploy federated learning models would use this.
No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer struggling with poor convergence or unstable performance in federated learning models due to non-IID (non-identically and independently distributed) data across your clients.
Not ideal if your federated learning data is already identically and independently distributed across clients, or if you are not working with federated learning at all.
Stars
8
Forks
—
Language
Python
License
—
Category
Last pushed
Feb 13, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ChristianInterno/FedLEx"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
flwrlabs/flower
Flower: A Friendly Federated AI Framework
JonasGeiping/breaching
Breaching privacy in federated learning scenarios for vision and text
zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on...
anupamkliv/FedERA
FedERA is a modular and fully customizable open-source FL framework, aiming to address these...
p2pfl/p2pfl
P2PFL is a decentralized federated learning library that enables federated learning on...