Nokia-Bell-Labs/data-centric-federated-learning
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
This framework helps machine learning engineers and researchers train deep neural networks more efficiently on a wide variety of personal devices, like smartphones or IoT sensors, without compromising user privacy. It takes raw data from many decentralized devices and, through a selective process, produces a more accurate and faster-to-train machine learning model. This is designed for those managing large-scale federated learning deployments.
No commits in the last 6 months.
Use this if you are developing or deploying machine learning models across many resource-constrained devices and need to improve training efficiency and model accuracy.
Not ideal if your machine learning models are trained on centralized, powerful servers with abundant computational resources and no privacy constraints.
Stars
13
Forks
3
Language
Python
License
BSD-3-Clause-Clear
Category
Last pushed
Apr 15, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Nokia-Bell-Labs/data-centric-federated-learning"
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
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...