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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.20598 |
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| _version_ | 1866912251664924672 |
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| author | Mondal, Sourav Wong, Elaine |
| author_facet | Mondal, Sourav Wong, Elaine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Coordinated Learning for H2M/R Applications over Optical Access Networks (Invited) Mondal, Sourav Wong, Elaine Networking and Internet Architecture Artificial Intelligence 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. |
| title | Scalable Coordinated Learning for H2M/R Applications over Optical Access Networks (Invited) |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2502.20598 |