Omid Abbasi

Amin Farajzadeh, Animesh Yadav, Omid Abbasi et al.

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

Weili Wang, Omid Abbasi, Halim Yanikomeroglu et al.

Vertical heterogenous networks (VHetNets) and artificial intelligence (AI) play critical roles in 6G and beyond networks. This article presents an AI-native VHetNets architecture to enable the synergy of VHetNets and AI, thereby supporting varieties of AI services while facilitating automatic and intelligent network management. Anomaly detection in Internet of Things (IoT) is a major AI service required by many fields, including intrusion detection, state monitoring, device-activity analysis, security supervision and so on. Conventional anomaly detection technologies mainly consider the anomaly detection as a standalone service that is independent of any other network management functionalities, which cannot be used directly in ubiquitous IoT due to the resource constrained end nodes and decentralized data distribution. In this article, we develop an AI-native VHetNets-enabled framework to provide the anomaly detection service for ubiquitous IoT, whose implementation is assisted by intelligent network management functionalities. We first discuss the possibilities of VHetNets used for distributed AI model training to provide anomaly detection service for ubiquitous IoT, i.e., VHetNets for AI. After that, we study the application of AI approaches in helping provide automatic and intelligent network management functionalities for VHetNets, i.e., AI for VHetNets, whose aim is to facilitate the efficient implementation of anomaly detection service. Finally, a case study is presented to demonstrate the efficiency and effectiveness of the proposed AI-native VHetNets-enabled anomaly detection framework.