Yuanming Shi
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View article: Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning
Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning Open
This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates multiple edge devices to wirelessly sense the objects and use…
View article: Satellite edge artificial intelligence with large models: architectures and technologies
Satellite edge artificial intelligence with large models: architectures and technologies Open
View article: Space Computing Power Networks: Fundamentals and Techniques
Space Computing Power Networks: Fundamentals and Techniques Open
View article: Edge Large AI Models: Collaborative Deployment and IoT Applications
Edge Large AI Models: Collaborative Deployment and IoT Applications Open
Large artificial intelligence models (LAMs) emulate human-like problem-solving capabilities across diverse domains, modalities, and tasks. By leveraging the communication and computation resources of geographically distributed edge devices…
View article: Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks
Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks Open
Pre-trained foundation models (FMs), with extensive number of neurons, are key to advancing next-generation intelligence services, where personalizing these models requires massive amount of task-specific data and computational resources. …
View article: Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks
Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks Open
Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by t…
View article: A Survey on Integrated Sensing, Communication, and Computation
A Survey on Integrated Sensing, Communication, and Computation Open
The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligen…
View article: Structured IB: Improving Information Bottleneck with Structured Feature Learning
Structured IB: Improving Information Bottleneck with Structured Feature Learning Open
The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across image segmentation, document clustering…
View article: Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces Empowered Cooperative Rate Splitting with User Relaying
Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces Empowered Cooperative Rate Splitting with User Relaying Open
In this work, we unveil the advantages of synergizing cooperative rate splitting (CRS) with user relaying and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR RIS). Specifically, we propose a novel STAR R…
View article: Hierarchical Learning and Computing over Space-Ground Integrated Networks
Hierarchical Learning and Computing over Space-Ground Integrated Networks Open
Space-ground integrated networks hold great promise for providing global connectivity, particularly in remote areas where large amounts of valuable data are generated by Internet of Things (IoT) devices, but lacking terrestrial communicati…
View article: A Survey on Integrated Sensing, Communication, and Computation
A Survey on Integrated Sensing, Communication, and Computation Open
The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modul…
View article: Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis Open
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth …
View article: Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks
Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks Open
Pre-trained foundation models (FMs), with extensive number of neurons, are key to advancing next-generation intelligence services, where personalizing these models requires massive amount of task-specific data and computational resources. …
View article: Toward Effective and Interpretable Semantic Communications
Toward Effective and Interpretable Semantic Communications Open
With the exponential surge in traffic data and the pressing need for\nultra-low latency in emerging intelligence applications, it is envisioned that\n6G networks will demand disruptive communication technologies to foster\nubiquitous intel…
View article: Collaborative Edge AI Inference over Cloud-RAN
Collaborative Edge AI Inference over Cloud-RAN Open
In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the…
View article: A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids Open
The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These ch…
View article: Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
Satellite Federated Edge Learning: Architecture Design and Convergence Analysis Open
The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth …
View article: A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids Open
The integration of various power sources, including renewables and electric vehicles, into smart grids is expanding, introducing uncertainties that can result in issues like voltage imbalances, load fluctuations, and power losses. These ch…
View article: Federated Linear Bandit Learning via Over-the-air Computation
Federated Linear Bandit Learning via Over-the-air Computation Open
In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, an…
View article: Integrating Sensing, Communication, and Power Transfer: From Theory to Practice
Integrating Sensing, Communication, and Power Transfer: From Theory to Practice Open
To support the development of internet-of-things applications, an enormous population of low-power devices are expected to be incorporated in wireless networks performing sensing and communication tasks. As a key technology for improving t…
View article: Decentralized Over-the-Air Federated Learning by Second-Order Optimization Method
Decentralized Over-the-Air Federated Learning by Second-Order Optimization Method Open
Federated learning (FL) is an emerging technique that enables privacy-preserving distributed learning. Most related works focus on centralized FL, which leverages the coordination of a parameter server to implement local model aggregation.…
View article: One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation Open
Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies di…
View article: One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation Open
Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies di…
View article: Over-the-Air Federated Learning and Optimization
Over-the-Air Federated Learning and Optimization Open
Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (A…
View article: Towards Scalable Wireless Federated Learning: Challenges and Solutions
Towards Scalable Wireless Federated Learning: Challenges and Solutions Open
The explosive growth of smart devices (e.g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data. The generated massive data together with the rapid advan…
View article: Federated Linear Bandit Learning via Over-the-Air Computation
Federated Linear Bandit Learning via Over-the-Air Computation Open
In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, an…
View article: Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks
Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks Open
With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-…
View article: Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks
Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks Open
With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-…
View article: Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference
Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference Open
Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing ambient intelligent services. For enhancing the utilization of…
View article: Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
Integrated Sensing-Communication-Computation for Edge Artificial Intelligence Open
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of e…