Rate-Splitting Multiple Access (RSMA) has recently found favour in the multi-antenna-aided wireless downlink, as a benefit of relaxing the accuracy of Channel State Information at the Transmitter (CSIT), while in achieving high spectral efficiency and providing security guarantees. These benefits are particularly important in high-velocity vehicular platoons since their high Doppler affects the estimation accuracy of the CSIT. To tackle this challenge, we propose an RSMA-based Internet of Vehicles (IoV) solution that jointly considers platoon control and FEderated Edge Learning (FEEL) in the downlink. Specifically, the proposed framework is designed for transmitting the unicast control messages within the IoV platoon, as well as for privacy-preserving FEEL-aided downlink Non-Orthogonal Unicasting and Multicasting (NOUM). Given this sophisticated framework, a multi-objective optimization problem is formulated to minimize both the latency of the FEEL downlink and the deviation of the vehicles within the platoon. To efficiently solve this problem, a Block Coordinate Descent (BCD) framework is developed for decoupling the main multi-objective problem into two sub-problems. Then, for solving these non-convex sub-problems, a Successive Convex Approximation (SCA) and Model Predictive Control (MPC) method is developed for solving the FEEL-based downlink problem and platoon control problem, respectively. Our simulation results show that the proposed RSMA-based IoV system outperforms the conventional systems.
Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Things
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西智能网络与场景系统重点实验室（信息与通信学院，桂林电子科技大学））；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通运输大数据应用技术国家工程实验室（广西））；School of Automation, Guangdong University of Technology（自动化学院，广东工业大学）；School of Architecture and Transportation Engineering, GUET（建筑与交通工程学院，桂林电子科技大学）
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Electro-optical Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学光电工程学院）；Technische University（工业大学）
Fusion of Federated Learning and Industrial Internet of Things: A Survey
Vellore Institute of Technology（韦洛尔理工学院）；Pusan National University（釜山国立大学）；Trinity College Dublin（都柏林大学三一学院）；Kumoh National Institute of Technology（金乌国立技术学院）
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）；Information Technology R&D Center, Mitsubishi Electric Corporation（三菱电机株式会社信息技术研发中心）；NC State University（北卡罗来纳州立大学）
Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）；China Academy of Information and Communications Technology（中国信息通信研究院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Zhejiang University（浙江大学）
