Methods for Reliable Over-the-Air Computation and Federated Edge Learning


Reference #: 01538

The University of South Carolina is offering licensing opportunities for Methods for Reliable Over-the-Air Computation and Federated Edge Learning


Federated edge learning (FEEL) is a distributed learning framework that leverages the computational powers of edge devices (EDs) and uses the local data at the EDs without compromising their privacy to train a model. However, the communication aspect of FEEL stands as one of the main bottlenecks. To address this issue, one of the promising solutions is to perform the aggregation with over-the-air computation (AirComp) methods that harness the signal-superposition property of the wireless multiple-access channel.  

Invention Description:

This invention addresses the communication latency problem of training an artificial intelligence model over a wireless network. It reduces the latency with over-the-air computation. However, the invention does not use the channel information (e.g., channel frequency response) needed for wireless communication at the edge devices (e.g., a user) or edge server (e.g., a base station).

Potential Applications: 

An over-the-air computation (AirComp) scheme for federated edge learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or edge server (ES).

Advantages and Benefits:

The proposed scheme does not need a channel inversion at the EDs. From this aspect, it is compatible with time-varying channels and does not lose the gradient information due to the truncation. The proposed scheme reduces PMEPR with a simple randomization technique. it also does not require CSIs at the ES or multiple antennas for over-the-air computation.

Patent Information:
Title App Type Country Serial No. Patent No. File Date Issued Date Expire Date Patent Status
Methods for Reliable Over-the-Air Computation and Federated Edge Learning Utility United States 17/728,119   4/25/2022     Filed
For Information, Contact:
Technology Commercialization
University of South Carolina
Alphan Sahin
Everette Bryson
Safi Shams Muhtasimul Hoque
Distributed learning
federated edge learning
frequency-shift keying
orthogonal frequency division multiplexing
over-the-air computation
peak-to-mean envelope power ratio
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