An algorithm for model fusion for distributed learning

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.
  • Dinesh Verma (IBM US)
  • Supriyo Chakraborty (IBM US)
  • Seraphin Calo (IBM US)
  • Simon Julier (UCL)
  • Stephen Pasteris (UCL)
Date Apr-2018
Venue SPIE - Defense + Commercial Sensing 2018