alphansahin/Wireless-Federated-Learning-with-Non-coherent-Over-the-Air-Computation
This respository consists of the source codes that allow one to realize over-the-air computation for federated edge learning by using Adalm Pluto SDRs.
This project helps wireless communication researchers and engineers build and test federated learning systems that operate over shared wireless channels. It allows you to use low-cost software-defined radios (SDRs) to simulate and evaluate how well machine learning models can be trained collaboratively across multiple devices without perfect channel knowledge. You input data for federated learning tasks, and it outputs performance metrics like model accuracy.
No commits in the last 6 months.
Use this if you are a wireless communication researcher or engineer looking to experiment with and demonstrate over-the-air computation techniques for federated learning using hardware.
Not ideal if you are an end-user looking for a high-level machine learning framework or a software-only simulation of federated learning without hardware involvement.
Stars
19
Forks
8
Language
VHDL
License
—
Category
Last pushed
Apr 11, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/alphansahin/Wireless-Federated-Learning-with-Non-coherent-Over-the-Air-Computation"
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...