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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.
Dinesh Verma,Supriyo Chakraborty,Seraphin Calo,Simon Julier, andStephen Pasteris
"An algorithm for model fusion for distributed learning", Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350O (4 May 2018); https://doi.org/10.1117/12.2304542
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Dinesh Verma, Supriyo Chakraborty, Seraphin Calo, Simon Julier, Stephen Pasteris, "An algorithm for model fusion for distributed learning," Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350O (4 May 2018); https://doi.org/10.1117/12.2304542