The development of the 5G network and the transition to 6G has given rise to multiple challenges ... more The development of the 5G network and the transition to 6G has given rise to multiple challenges for ensuring high-quality and reliable network services. One of these main challenges is the emergent intelligent defined networks (IDN), designed to provide highly efficient connectivity, by merging artificial intelligence (AI) and networking concepts, to ensure distributed intelligence over the entire network. To this end, it will be necessary to develop and implement proper machine learning (ML) algorithms that take into account this new distributed nature of the network to represent increasingly dynamic, adaptable, scalable, and efficient systems. To be able to cope with more stringent service requirements, it is necessary to renew the ML approaches to make them more efficient and faster. Distributed learning (DL) approaches are shown to be effective in enabling the possibility of deploying intelligent nodes in a distributed network. Among several DL approaches, transfer learning (TL...
The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set to redefine conventiona... more The forthcoming 6G-enabled Intelligent Transportation System (ITS) is set to redefine conventional transportation networks with advanced intelligent services and applications. These technologies, including edge computing, Machine Learning (ML), and network softwarization, pose stringent requirements for latency, energy efficiency, and user data security. Distributed Learning (DL), such as Federated Learning (FL), is essential to meet these demands by distributing the learning process at the network edge. However, traditional FL approaches often require substantial resources for satisfactory learning performance. In contrast, Transfer Learning (TL) and Split Learning (SL) have shown effectiveness in enhancing learning efficiency in resourceconstrained wireless scenarios like ITS. Non-terrestrial Networks (NTNs) have recently acquired a central place in the 6G vision, especially for boosting the coverage, capacity, and resilience of traditional terrestrial facilities. Air-based NTN layers, such as High Altitude Platforms (HAPs), can have added advantages in terms of reduced transmission distances and flexible deployments and thus can be exploited to enable intelligent solutions for latency-critical vehicular scenarios. With this motivation, in this work, we introduce the concept of Federated Split Transfer Learning (FSTL) in joint air-ground networks for resourceconstrained vehicular scenarios. Simulations carried out in vehicular scenarios validate the efficacy of FSTL on HAPs in NTN, demonstrating significant improvements in addressing the demands of ITS applications.
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Papers by David Naseh