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Enforcing Differential Privacy in Federated Learning via Long-term Contribution Incentives
Xiangyun Tang;Luyao Peng;Yu Weng;Meng Shen;Liehuang Zhu;Robert H. Deng
IEEE Transactions on Information Forensics and Security
Year: 2025 | Early Access Article |
Privacy-preserving Federated Learning (FL) based on Differential Privacy (DP) protects clients’ data by adding DP noise to samples’ gradients and has emerged as a de facto standard for data privacy in FL. However, the accuracy of global models in DP-based FL may be reduced significantly when rogue clients occur who deviate from the preset DP-based FL approaches and selfishly inject excessive DP no...Show More
LGVLM-mIoT: A Lightweight Generative Visual-Language Model for Multilingual IoT Applications
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
The demand for edge device models equipped with multilingual visual capabilities is rapidly increasing in complex IoT application scenarios. While many studies have endowed models with strong visual sensory and language analysis capabilities, these models are often large and require substantial amounts of data. Moreover, multilingual parallel corpora are extremely scarce. Although large parameter ...Show More
Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey
Tao Zhang;Fanyu Kong;Dongshang Deng;Xiangyun Tang;Xuangou Wu;Changqiao Xu;Liehuang Zhu;Jiqiang Liu;Bo Ai;Zhu Han;Robert H. Deng
IEEE Internet of Things Journal
Year: 2025 | Early Access Article |
Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and D...Show More
Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients
GLOBECOM 2024 - 2024 IEEE Global Communications Conference
Year: 2024 | Conference Paper |
Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have pr...Show More
Enabling Trustable Financing: A Verifiable Privacy-Preserving Cross-Chain Protocol
Lidu Lou;Wenbin He;Xiangyun Tang;Yiying Yang;Tao Zhang;Yanyu Cheng;Cai Xu
2024 IEEE/CIC International Conference on Communications in China (ICCC Workshops)
Year: 2024 | Conference Paper |
Generative AI-enabled wireless communications enhance efficiency and offer new tools for supply chain finance (SCF) innovation. SCF optimizes capital efficiency and reduces risk, but information asymmetry hinders risk assessment. Blockchain technology addresses these challenges by solving information asymmetry and regulatory issues. However, issues of privacy protection, reliability, and traceabil...Show More
Energy Efficiency Maximization for Secure Live Video Streaming in UAV Wireless Networks
Lan Yi;Jiansong Miao;Tao Zhang;Yushun Yao;Xiangyun Tang;Zaodi Song
2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
Year: 2024 | Conference Paper |
Unmanned aerial vehicles (UAVs) have shown great potential in live video streaming applications, especially in surveillance and reconnaissance. However, ensuring high quality of service (QoS) remains a challenge due to the dynamic nature of wireless channels. In this paper, we tackle the crucial challenge of energy-efficient and secure UAV-enabled live video streaming. To maximize long-term energy...Show More
FedASA: A Personalized Federated Learning With Adaptive Model Aggregation for Heterogeneous Mobile Edge Computing
Dongshang Deng;Xuangou Wu;Tao Zhang;Xiangyun Tang;Hongyang Du;Jiawen Kang;Jiqiang Liu;Dusit Niyato
Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data. However, deploying FL frameworks in real IoT scenarios faces three critical challenges, i.e., statistical heterogeneity, resource constraint, and fairness. To address these challenges, we design a fair and effici...Show More
QoE Maximization for Video Streaming in Cache-Enable Satellite-UAV-Terrestrial Network
Zhibo Yang;Jiansong Miao;Tao Zhang;Xiangyun Tang;Jiawen Kang;Dusit Niyato
ICC 2024 - IEEE International Conference on Communications
Year: 2024 | Conference Paper |
Unmanned aerial vehicle (UAV)-assisted video streaming is gaining growing interests in satellite-terrestrial networks due to the mobility and caching capability. However, it is challenging to perform trajectory planning and cache management towards maximizing quality of experience (QoE) for video streaming due to a dynamic network topology and a class of hybrid control actions. In this paper, we c...Show More
Towards Secrecy Energy-Efficient RIS Aided UAV Network: A Lyapunov-Guided Reinforcement Learning Approach
Yushun Yao;Jiansong Miao;Tao Zhang;Xiangyun Tang;Jiawen Kang;Dusit Niyato
2024 IEEE Wireless Communications and Networking Conference (WCNC)
Year: 2024 | Conference Paper |
Unmanned aerial vehicles (UAVs) are integrated into existing networks to enhance coverage, increase network capacity and provide ubiquitous access service. However, the channel in the UAV network is prone to noise and interference due to the complex environments. Reconfigurable intelligent surface (RIS), as an emerging technology in recent years, can be applied to the UAV network to establish the ...Show More
Secure Semantic Communications: Challenges, Approaches, and Opportunities
Meng Shen;Jing Wang;Hongyang Du;Dusit Niyato;Xiangyun Tang;Jiawen Kang;Yaoling Ding;Liehuang Zhu
Semantic communication has emerged as a promising technique that can integrate the meaning and context of information into a communication process. It goes beyond raw data transmission and enables a sender and a receiver to transmit and recover semantic messages through their respective knowledge bases. However, it is challenging to achieve secure semantic communication due to complicated security...Show More
Confidence-Aware Sentiment Quantification via Sentiment Perturbation Modeling
Xiangyun Tang;Dongliang Liao;Meng Shen;Liehuang Zhu;Shen Huang;Gongfu Li;Hong Man;Jin Xu
Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a target. Existing methods equally treat and aggregate individual reviews’ sentiment to judge the overall sentiment polarity. However, the confidence of each review is not equal in sentiment quantification where sentiment perturbation arising from high- and low-confidence reviews ...Show More
Federated Learning(FL) is a promising distributed learning architecture. However, it faces significant threats from malicious attacks, including adversarial samples and backdoor attacks. Although some work has proposed defences against these two types of attacks, there are already attacks that combine the two, known as synergetic attacks. This synergetic attack typically uses adversarial samples t...Show More
Decentralized Reputation-based Leader Election for Privacy-preserving Federated Learning on Internet of Things
Luyao Peng;Xiangyun Tang;Chenxi Li;Yao Xiao;Tao Zhang;Yu Weng
The Internet of Things (IoT) based on Deep Learning (DL) technologies has facilitated people’s lives in various aspects. However, since the IoT data used to train DL models contains users’ sensitive personal information, privacy and security concerns arise in IoT. Although many works have presented security solutions for the privacy and security concerns on IoT, they cannot monitor the model quali...Show More
Images captured in low-light settings often manifest issues, notably reduced brightness and amplified noise. Addressing these concerns, our research elucidates a sophisticated technique crafted to enhance the fidelity of images taken under such adverse circumstances. During the image acquisition phase, the camera sensor meticulously logs the raw RGB data. This information subsequently undergoes a ...Show More
Exploring the Impact of Non-IID on Federated Learning
Minyang Li;Xiangyun Tang;Siru Chen;Yu Weng;Luyao Peng;Wen Yang
2023 International Conference on Blockchain Technology and Information Security (ICBCTIS)
Year: 2023 | Conference Paper |
In the past few years, the growing problem of data silos has given rise to an emerging framework for distributed deep learning: Federated learning. The framework enables the construction of global models across multiple participants without the need for directly sharing raw data. However, because the environments oriented towards Federated learning are usually complex and heterogeneous, resulting ...Show More
Blockchains for Artificial Intelligence of Things: A Comprehensive Survey
Meng Shen;Aijing Gu;Jiawen Kang;Xiangyun Tang;Xiaodong Lin;Liehuang Zhu;Dusit Niyato
IEEE Internet of Things Journal
Cited by: Papers (22)
With the rapid advances in information and communication technologies, the Internet of Things (IoT) has become large and complex, bearing tremendous amounts of data and running devices in various scenarios. Leveraging artificial intelligence (AI) technologies, IoT can achieve superior information extraction, data analytics, and decision making, which has resulted in the revolutionized AI of Things...Show More
PILE: Robust Privacy-Preserving Federated Learning Via Verifiable Perturbations
Xiangyun Tang;Meng Shen;Qi Li;Liehuang Zhu;Tengfei Xue;Qiang Qu
Federated learning (FL) protects training data in clients by collaboratively training local machine learning models of clients for a global model, instead of directly feeding the training data to the server. However, existing studies show that FL is vulnerable to various attacks, resulting in training data leakage or interfering with the model training. Specifically, an adversary can analyze local...Show More
Secure and Trusted Collaborative Learning Based on Blockchain for Artificial Intelligence of Things
Xiangyun Tang;Liehuang Zhu;Meng Shen;Jialiang Peng;Jiawen Kang;Dusit Niyato;Ahmed A. Abd El-Latif
IEEE Wireless Communications
Cited by: Papers (19)
Empowered by promising artificial intelligence, the traditional Internet of Things is evolving into the Artificial Intelligence of Things (AIoT), which is an important enabling technology for Industry 4.0. Collaborative learning is a key technology for AIoT to build machine learning (ML) models on distributed datasets. However, there are two critical concerns of collaborative learning for AIoT: pr...Show More
Privacy-preserving Training Algorithm for Naive Bayes Classifiers
ICC 2022 - IEEE International Conference on Communications
Year: 2022 | Conference Paper |
The growing popularity of Machine learning (ML) that appreciates high quality training datasets collected from multiple organizations raises natural questions about the privacy guarantees that can be provided in such settings. Our work tackles this problem in the context of multi-party secure ML wherein multiple organizations provide their sensitive datasets to a data user and train a Naive Bayes ...Show More
Privacy-Preserving Machine Learning Training in IoT Aggregation Scenarios
Liehuang Zhu;Xiangyun Tang;Meng Shen;Feng Gao;Jie Zhang;Xiaojiang Du
IEEE Internet of Things Journal
Cited by: Papers (23)
In developing smart city, the growing popularity of machine learning (ML) that appreciates high-quality training data sets generated from diverse Internet-of-Things (IoT) devices raises natural questions about the privacy guarantees that can be provided in such settings. Privacy-preserving ML training in an aggregation scenario enables a model demander to securely train ML models with the sensitiv...Show More
Secure SVM Training Over Vertically-Partitioned Datasets Using Consortium Blockchain for Vehicular Social Networks
Machine learning (ML) techniques are expected to be used for specific applications in Vehicular Social Networks (VSNs). Support vector machine (SVM) is one of the typical ML methods and widely used for its high efficiency. Due to the limitation of data sources, the data collected by different entities usually contain attributes that are quite different. However, in some real-world scenarios, when ...Show More
Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities
Meng Shen;Xiangyun Tang;Liehuang Zhu;Xiaojiang Du;Mohsen Guizani
IEEE Internet of Things Journal
Cited by: Papers (346)
Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a coll...Show More
Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks
Liehuang Zhu;Xiangyun Tang;Meng Shen;Xiaojiang Du;Mohsen Guizani
Existing distributed denial-of-service attack detection in software defined networks (SDNs) typically perform detection in a single domain. In reality, abnormal traffic usually affects multiple network domains. Thus, a cross-domain attack detection has been proposed to improve detection performance. However, when participating in detection, the domain of each SDN needs to provide a large amount of...Show More
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