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

--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. In recent years, several inter-disciplinary technical paradigms like cyber-physical systems, Industrial IoT, robotics, big data, cloud/edge and cognitive computing, and virtual/augmented reality (VR/AR) have received significant attention from both industry and academia. The primary reason behind this development is the inclusion of industry vertical scenarios like Industry 4.0 in the fifth and beyond-fifth generation mobile technologies [1]. Although Industry 4.0 primarily involved connectivity among cyber-physical systems, Industry 5.0 will focus on the "human and machine/robots/cobots" relationship [2] to ensure real-time monitoring of products' condition, use, and the environment through sensors and external data sources, dynamic control of product functions and personalized user experience through embedded software in the products, optimization of use and performance of products, and autonomous delivery of products through coordinated operations with other products and systems.