Fair Resource Allocation For Hierarchical Federated Edge Learning in Space-Air-Ground Integrated Networks via Deep Reinforcement Learning with Hybrid Control
The space-air-ground integrated network (SAGIN) has become a crucial research direction in future wireless communications due to its ubiquitous coverage, rapid and flexible deployment, and multi-layer cooperation capabilities. However, integrating hierarchical federated learning (HFL) with edge computing and SAGINs remains a complex open issue to be resolved. This paper proposes a novel framework for applying HFL in SAGINs, utilizing aerial platforms and low Earth orbit (LEO) satellites as edge servers and cloud servers, respectively, to provide multi-layer aggregation capabilities for HFL. The proposed system also considers the presence of inter-satellite links (ISLs), enabling satellites to exchange federated learning models with each other. Furthermore, we consider multiple different computational tasks that need to be completed within a limited satellite service time. To maximize the convergence performance of all tasks while ensuring fairness, we propose the use of the distributional soft-actor-critic (DSAC) algorithm to optimize resource allocation in the SAGIN and aggregation weights in HFL. Moreover, we address the efficiency issue of hybrid action spaces in deep reinforcement learning (DRL) through a decoupling and recoupling approach, and design a new dynamic adjusting reward function to ensure fairness among multiple tasks in federated learning. Simulation results demonstrate the superiority of our proposed algorithm, consistently outperforming baseline approaches and offering a promising solution for addressing highly complex optimization problems in SAGINs.
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Arizona State University（亚利桑那州立大学）；The Hong Kong University of Science and Technology（香港科技大学）
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Communication-Learning Co-Design for Differentially Private Over-the-Air Federated Distillation
Department of Information Engineering, The Chinese University of Hong Kong（信息工程系，香港中文大学）；Intelligent Transportation Thrust, The Hong Kong University of Science and Technology (Guangzhou)（智能交通研究组，香港科学与技术大学（广州））
Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（皇家理工学院电气工程与计算机科学学院）；Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）
AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems
Department of Electrical Engineering, Chalmers University of Technology（楚德大学电气工程系）；School of Electrical and Computer Engineering, Purdue University（普渡大学电气与计算机工程学院）
