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Search Results - over-the-air+computation
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Methods and Procedures for Synchronization and
Over-the-Air Computation
Reference #: 01615 The University of South Carolina is offering licensing opportunities for Methods and Procedures for Synchronization and
Over-the-Air Computation
Background: Over-the-air computation (OAC) reduces the communication latency that linearly increases with the number of devices in a wireless network for machine learning applications....
Published: 2/28/2024
|
Inventor(s):
Alphan Sahin
Keywords(s):
federated learning
,
non-coherent computation
,
over-the-air computation
,
software-defined radios
,
sychronization
Category(s):
Software and Computing
,
Engineering and Physical Sciences
Over-the-Air Computation
Methods Based on Balanced Number Systems for FEEL
Reference #: 01612 The University of South Carolina is offering licensing opportunities for
Over-the-Air Computation
Methods Based on Balanced Number Systems for FEEL Background: 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...
Published: 10/24/2022
|
Inventor(s):
Alphan Sahin
Keywords(s):
federated learning
,
non-coherent computation
,
over-the-air computation
,
quantization
,
signed-digit number system
Category(s):
Software and Computing
,
Engineering and Physical Sciences
Methods for Long-Range Federated Edge Learning with Chirp-Based
Over-the-Air Computation
Reference #: 01574 The University of South Carolina is offering licensing opportunities for Methods for Long-Range Federated Edge Learning with Chirp-Based
Over-the-Air Computation
Background: Federated edge learning (FEEL) deploys federated learning (FL) over a wireless network, in which many edge devices (EDs) participate in training using locally...
Published: 1/3/2023
|
Inventor(s):
Safi Shams Muhtasimul Hoque
,
Alphan Sahin
Keywords(s):
chirp
,
DFT-s-OFDM
,
Distributed learning
,
federated edge learning
,
LoRa
,
orthogonal frequency division multiplexing
,
over-the-air computation
,
peak-to-mean envelope power ratio
,
pulse-position modulation
Category(s):
Engineering and Physical Sciences
,
Software and Computing
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 Background: 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...
Published: 5/9/2023
|
Inventor(s):
Mohammad Hassan Adeli
,
Alphan Sahin
Keywords(s):
Distributed learning
,
federated edge learning
,
frequency-shift keying
,
orthogonal frequency division multiplexing
,
over-the-air computation
,
peak-to-mean envelope power ratio
Category(s):
Engineering and Physical Sciences
,
Software and Computing
Methods for Reliable
Over-the-Air Computation
with Pulses for Distributed Learning
Reference #: 01541 The University of South Carolina is offering licensing opportunities for Methods for Reliable
Over-the-Air Computation
with Pulses for Distributed Learning Background: Federated edge learning (FEEL) is an implementation of federated learning (FL) over a wireless network to train a model by using the local data at the edge devices...
Published: 9/3/2022
|
Inventor(s):
Alphan Sahin
,
Everette Bryson
,
Safi Shams Muhtasimul Hoque
Keywords(s):
DFT-s-OFDM
,
Distributed learning
,
federated edge learning
,
orthogonal frequency division multiplexing
,
over-the-air computation
,
peak-to-mean envelope power ratio
,
pulse-position modulation
,
SC-FDE
Category(s):
Software and Computing
,
Engineering and Physical Sciences
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 Background: 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...
Published: 9/3/2022
|
Inventor(s):
Alphan Sahin
,
Everette Bryson
,
Safi Shams Muhtasimul Hoque
Keywords(s):
Distributed learning
,
federated edge learning
,
frequency-shift keying
,
orthogonal frequency division multiplexing
,
over-the-air computation
,
peak-to-mean envelope power ratio
Category(s):
Engineering and Physical Sciences
,
Software and Computing
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