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Proceedings Paper

An algorithm for model fusion for distributed learning
Author(s): Dinesh Verma; Supriyo Chakraborty; Seraphin Calo; Simon Julier; Stephen Pasteris
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Paper Abstract

In this paper, we discuss the problem of distributed learning for coalition operations. We consider a scenario where different coalition forces are running learning systems independently but want to merge the insights obtained from all the learning systems to share knowledge and use a single model combining all of their individual models. We consider the challenges involved in such fusion of models, and propose an algorithm that can find the right fused model in an efficient manner.

Paper Details

Date Published: 4 May 2018
PDF: 8 pages
Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350O (4 May 2018); doi: 10.1117/12.2304542
Show Author Affiliations
Dinesh Verma, IBM Thomas J. Watson Research Ctr. (United States)
Supriyo Chakraborty, IBM Thomas J. Watson Research Ctr. (United States)
Seraphin Calo, IBM Thomas J. Watson Research Ctr. (United States)
Simon Julier, Univ. College London (United Kingdom)
Stephen Pasteris, Univ. College London (United Kingdom)


Published in SPIE Proceedings Vol. 10635:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX
Michael A. Kolodny; Dietrich M. Wiegmann; Tien Pham, Editor(s)

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