aioz-ai/FADNet
Deep Federated Learning for Autonomous Driving (IV'22)
This project helps automotive engineers and researchers develop autonomous driving systems that prioritize user privacy. It takes real-world driving data from various vehicles and trains a shared driving policy model without centralizing sensitive user information. The output is a robust, privacy-preserving model that guides autonomous vehicles, making it ideal for organizations working on next-generation self-driving technology.
No commits in the last 6 months.
Use this if you need to train high-accuracy autonomous driving models using decentralized vehicle data while ensuring user privacy and complying with data protection regulations.
Not ideal if your autonomous driving development does not involve distributed data sources or if you do not have strict privacy requirements for training data.
Stars
38
Forks
6
Language
Python
License
MIT
Category
Last pushed
Jan 30, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aioz-ai/FADNet"
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...