Search Results for author: Zehui Xiong

Found 20 papers, 0 papers with code

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

no code implementations18 Feb 2022 Zhilin Wang, Qin Hu, Ruinian Li, Minghui Xu, Zehui Xiong

Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed.

Federated Learning

Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

no code implementations3 Jan 2022 Zhe Zhang, Shiyao Ma, Zhaohui Yang, Zehui Xiong, Jiawen Kang, Yi Wu, Kejia Zhang, Dusit Niyato

This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model.

Federated Learning

A Contract Theory based Incentive Mechanism for Federated Learning

no code implementations12 Aug 2021 Mengmeng Tian, Yuxin Chen, YuAn Liu, Zehui Xiong, Cyril Leung, Chunyan Miao

It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients.

Federated Learning

Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems

no code implementations2 Feb 2021 Weiheng Jiang, Yu Zhang, Jun Zhao, Zehui Xiong, Zhiguo Ding

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs).

Information Theory Signal Processing Information Theory

Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

no code implementations23 Dec 2020 Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Qingqing Wu, Massimo Tornatore, Stefano Secci

Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated.

reinforcement-learning

Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications

no code implementations18 Dec 2020 Yulan Gao, Chao Yong, Zehui Xiong, Dusit Niyato, Yue Xiao, Jun Zhao

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs).

Cross-Layer Coordinated Attacks on Cyber-Physical Systems: A LQG Game Framework with Controlled Observations

no code implementations4 Dec 2020 Yunhan Huang, Zehui Xiong, Quanyan Zhu

On the other hand, the interactions between the attacker and the defender in the physical layer significantly impact the observation and jamming strategies.

Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management

no code implementations28 Nov 2020 Helin Yang, Jun Zhao, Zehui Xiong, Kwok-Yan Lam, Sumei Sun, Liang Xiao

However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training.

Distributed Computing Federated Learning

Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework

no code implementations10 Aug 2020 Jiawen Kang, Zehui Xiong, Chunxiao Jiang, Yi Liu, Song Guo, Yang Zhang, Dusit Niyato, Cyril Leung, Chunyan Miao

This framework can achieve scalable and flexible decentralized FEL by individually manage local model updates or model sharing records for performance isolation.

Cryptography and Security

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

no code implementations19 Jul 2020 Yi Liu, Sahil Garg, Jiangtian Nie, Yang Zhang, Zehui Xiong, Jiawen Kang, M. Shamim Hossain

Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency.

Anomaly Detection Federated Learning +1

Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles

no code implementations13 Jul 2020 Jer Shyuan Ng, Wei Yang Bryan Lim, Hong-Ning Dai, Zehui Xiong, Jianqiang Huang, Dusit Niyato, Xian-Sheng Hua, Cyril Leung, Chunyan Miao

The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs.

Networking and Internet Architecture Signal Processing

Federated Learning for 6G Communications: Challenges, Methods, and Future Directions

no code implementations4 Jun 2020 Yi Liu, Xingliang Yuan, Zehui Xiong, Jiawen Kang, Xiaofei Wang, Dusit Niyato

As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications.

Federated Learning

Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach

no code implementations8 Apr 2020 Wei Yang Bryan Lim, Jianqiang Huang, Zehui Xiong, Jiawen Kang, Dusit Niyato, Xian-Sheng Hua, Cyril Leung, Chunyan Miao

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built.

Signal Processing Networking and Internet Architecture

Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

no code implementations27 Feb 2020 Helin Yang, Zehui Xiong, Jun Zhao, Dusit Niyato, Liang Xiao, Qingqing Wu

As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments.

reinforcement-learning

Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting

no code implementations7 Dec 2019 Huimei Han, Jun Zhao, Zehui Xiong, Dusit Niyato, Wenchao Zhai, Marco Di Renzo, Quoc-Viet Pham, Weidang Lu

Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS.

Reliable Federated Learning for Mobile Networks

no code implementations14 Oct 2019 Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, Mohsen Guizani

Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks.

Cryptography and Security

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

no code implementations16 May 2019 Jiawen Kang, Zehui Xiong, Dusit Niyato, Han Yu, Ying-Chang Liang, Dong In Kim

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.

Federated Learning

A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks

no code implementations7 May 2018 Wenbo Wang, Dinh Thai Hoang, Peizhao Hu, Zehui Xiong, Dusit Niyato, Ping Wang, Yonggang Wen, Dong In Kim

This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks.

Cryptography and Security

Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach

no code implementations8 Nov 2017 Nguyen Cong Luong, Zehui Xiong, Ping Wang, Dusit Niyato

However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open.

Computer Science and Game Theory

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