Methods for Multi-cell Over-the-Air Computation for Distributed Learning


Reference #: 01573

The University of South Carolina is offering licensing opportunities for Methods for Multi-cell Over-the-Air Computation for Distributed 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.

Invention Description:

This invention addresses the communication latency problem of training an artificial intelligence model together with the scalability issue of a single-cell wireless network. It reduces the latency with over-the-air computation and deploys a multi-cell network.

Potential Applications:

The technology could be useful for artificial intelligence technologies over wireless or sensor networks, 5G and beyond, 6G wireless standardization, IEEE 802.11 Wi-Fi.

Advantages and Benefits:

This scheme does not need a channel inversion at EDs and ESs. From this aspect, it is compatible with time-varying channels and does not lose the gradient information due to the truncation. It also does not require CSIs at both EDs and ESs or multiple antennas for over-the-air computation. Moreover, in our user-centric approach with heterogeneous data distribution, EDs can learn to classify labels of their data without availability of some digits on their datasets.


Patent Information:
For Information, Contact:
Technology Commercialization
University of South Carolina
Mohammad Hassan Adeli
Alphan Sahin
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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