Dynamic Resource Allocation-Enabled Distributed Learning as a Service for Vehicular Networks

The growth and development of 6 G networks bring a crucial task onto the table which is introducing AI to manage network functions and cope with more users in the future. Distributed Learning (DL), as service to technologies, acts as one of the overriding forces motivating the development of 6 G communication system. The 6 G will help in various applications, which are a large set of services; then you will be able to manage the network in a smart and dynamic way. While relatively, many deep learning (DL) methods is deployed within the resource constrained vehicular domain, this setting nonetheless poses significant challenges. The reciprocating development of distributed computing and communication resources, like the edge-cloud continuum and integrated terrestrial-non-terrestrial networks (T/NTN), offers a solution for this. To use these resources and multiple DL methods in an integrated way, the NS becomes the best option. This paper covers the methods of DL that apply best in vehicular environments and examines NS roles alongside these methods, especially in the context of resource management which is dynamic. This research is designed to build a architecture for DL-as-a-Service (DLaaS) letting it to be hosted on a distributed network platform and allowing the implementation of DL algorithms at a proactive basis. This method, in addition to the dynamic resource allocation strategies, permits the efficient control of the different services which have different requirements. Effectiveness of the model is exhibited in a full case step by step in a vehicular T/NTN. The run-down of the DLaaS approach is perceived from its strong points, namely flexibility, performance upgrades, improved network intelligence, and they lead eventually to heightened customer satisfaction from the given cross vehicle or non cross vehicle highway traffic scenarios.