Verma, D;
Chakraborty, S;
Calo, S;
Julier, S;
Pasteris, S;
(2018)
An algorithm for model fusion for distributed learning.
In: Kolodny, Michael A. and Wiegmann, Dietrich M. and Pham, Tien, (eds.)
Proceedings of Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX; 106350O (2018).
SPIE: Florida, United States.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | An algorithm for model fusion for distributed learning |
| Event: | Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX; 106350O (2018) |
| ISBN-13: | 9781510617810 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/12.2304542 |
| Publisher version: | https://doi.org/10.1117/12.2304542 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | distributed learning, coalition operations, federated learning, data efficiency |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10059463 |
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