Scalable Coordinated Learning for H2M/R Applications over Optical Access Networks (Invited)
This addresses the challenge of efficient and scalable collaborative communications in next-generation fiber-wireless access networks for Industry 5.0 applications.
The paper tackles the problem of enabling scalable human-to-machine/robot communications over large distances for Industry 5.0 by proposing a coordinated learning approach, which saves approximately 72% in training time for rapid onboarding of new machines/robots.
One of the primary research interests adhering to next-generation fiber-wireless access networks is human-to-machine/robot (H2M/R) collaborative communications facilitating Industry 5.0. This paper discusses scalable H2M/R communications across large geographical distances that also allow rapid onboarding of new machines/robots as $\sim72\%$ training time is saved through global-local coordinated learning.