# Scalable Coordinated Learning for H2M/R Applications over Optical Access Networks (Invited)

arXiv: https://arxiv.org/abs/2502.20598 · HTML: https://mlsanity.com/paper/2502.20598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)

## Abstract (arXiv)

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 ~72% training time is saved through global-local coordinated learning.