A5025203648- IoT and Edge/Fog Computing
- Privacy-Preserving Technologies in Data
- Age of Information Optimization
- Advanced Wireless Communication Technologies
- Stochastic Gradient Optimization Techniques
- Brain Tumor Detection and Classification
- Cloud Computing and Resource Management
- Telecommunications and Broadcasting Technologies
- Advanced MIMO Systems Optimization
- Mobile Crowdsensing and Crowdsourcing
- Blockchain Technology Applications and Security
- Distributed and Parallel Computing Systems
- IoT-based Smart Home Systems
- Cryptography and Data Security
- Distributed systems and fault tolerance
- Caching and Content Delivery
- Advanced Computing and Algorithms
Hong Kong University of Science and Technology
2022-2024
University of Hong Kong
2022-2024
ShanghaiTech University
2022-2023
University of Chinese Academy of Sciences
2022
Shanghai Institute of Microsystem and Information Technology
2022
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources improving spectrum energy efficiency. How to effectively address diverse user requirements guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem utilizing heterogenous pervasive intelligence support everyone-centric customized services anywhere anytime. In article, we first coin the...
Federated Learning (FL) has emerged as a fundamental learning paradigm to harness massive data scattered at geo-distributed edge devices in privacy-preserving way. Given the heterogeneous deployment of devices, however, their are usually Non-IID, introducing significant challenges FL including degraded training accuracy, intensive communication costs, and high computing complexity. Towards that, traditional approaches typically utilize adaptive mechanisms, which may suffer from scalability...
To cope with the high communication overhead caused by frequent aggregation of Federated Learning (FL) in Multi-access Edge Computing (MEC) scenarios, Hierarchical (HFEL) is proposed as an evolving framework. HFEL offloads tasks to edge servers for partial model reduce network traffic. However, most existing research focuses on resource optimization without considering impact data characteristics and cannot guarantee quality FL training. this end, we propose a task offloading approach based...
With the massive use of GPU, task scheduling under CPU-GPU clusters has become an indispensable research topic. Unlike existing models, we propose innovative framework that users offload their tasks in heterogeneous Edge Clusters (ECs) instead general-purpose CPU clusters. The takes full advantage GPU's powerful parallel computing capabilities. Specifically, decompose each user into sequential segments and segments, which can be offloaded to CPUs GPUs ECs, respectively. By dis-cretizing...
With the rapid development of edge data intelligence, task offloading (TO) and resource allocation (RA) optimization in multiaccess computing networks can significantly improve Quality Service (QoS). However, for online scenario, traditional methods (e.g., game theory numerical methods) cannot adapt to dynamic environments. Deep reinforcement learning (DRL) is applied adjust policy get long-term rewards. Nevertheless, since joint problem TO RA nonconvex NP-hard, existing DRL guarantee high...
Federated Learning (FL) has emerged as a fundamental learning paradigm to harness massive data scattered at geo-distributed edge devices in privacy-preserving way. Given the heterogeneous deployment of devices, however, their are usually Non-IID, introducing significant challenges FL including degraded training accuracy, intensive communication costs, and high computing complexity. Towards that, traditional approaches typically utilize adaptive mechanisms, which may suffer from scalability...
In the rapidly evolving field of Heterogeneous Multi-access Edge Computing (HMEC), efficient task offloading plays a pivotal role in optimizing system throughput and resource utilization. However, existing methods often fall short adequately modeling dependency topology relationships between offloaded tasks, which limits their effectiveness capturing complex interdependencies features. To address this limitation, we propose mechanism based on Graph Neural Networks (GNN). Our approach takes...
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources improving spectrum energy efficiency. How to effectively address diverse user requirements guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem utilizing heterogenous pervasive intelligence support everyone-centric customized services anywhere anytime. In article, we first coin the...
In the cloud data centers of large technology companies, there are various resources such as computing, communication and storage, which provide stable quality-guaranteed services for subscribers. To get a comprehensive understanding real load trends to observe characteristics user tasks consuming resources, we analyzed workload Microsoft Azure Virtual Machines (VMs). Specifically, all VMs in cluster within one month 2017 2019. The existing public provides complete information each schema....