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Federated Learning for Accurate Object Classification

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0860-22-C-7070
Agency Tracking Number: B212-020-0164
Amount: $154,994.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: MDA21-020
Solicitation Number: 21.2
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2021-12-06
Award End Date (Contract End Date): 2022-06-05
Small Business Information
7047 Old Madison Pike, Suite 305
Huntsville, AL 35806-2197
United States
DUNS: 968887195
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Mary Stewart
 (703) 994-5412
 mary.stewart@nou-systems.com
Business Contact
 Heather Johns
Phone: (256) 327-5541
Email: heather.johns@nou-systems.com
Research Institution
N/A
Abstract

nou Systems, Inc. proposes utilizing the Artificial Intelligence (AI techniques of Vertical Federated Learning and Split Learning to improve object classification This training paradigm will also accommodate multiple sources without duplicating data. A Federated Neural Network (FNN) or split learning AI approach would allow data from disparate sensors to train on their own observations and proved parameters and weights to a trusted third-party to do classification. FNN's are very robust to non-IID (Identically and Dependently Distributed) data, which is inherent to using multiple sensors placed at different locations, viewing different aspects and stages of flight, and trained on different threat databases. The technique will aid in better identifying threats not in an a-priori database. Approved for Public Release | 21-MDA-11013 (19 Nov 21)

* Information listed above is at the time of submission. *

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