Search Results for author: Wei Ni

Found 56 papers, 6 papers with code

Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction

no code implementations2 Jan 2025 Xi Yu, Tiejun Lv, Weicai Li, Wei Ni, Dusit Niyato, Ekram Hossain

The key idea is that we interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features.

Feature Correlation Graph Attention +1

Tabular Data Synthesis with Differential Privacy: A Survey

no code implementations4 Nov 2024 Mengmeng Yang, Chi-Hung Chi, Kwok-Yan Lam, Jie Feng, Taolin Guo, Wei Ni

This paper provides a comprehensive overview of existing differentially private tabular data synthesis methods, highlighting the unique challenges of each generation model for generating tabular data under differential privacy constraints.

Survey

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

no code implementations2 Oct 2024 Kai Li, Yilei Liang, Xin Yuan, Wei Ni, Jon Crowcroft, Chau Yuen, Ozgur B. Akan

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT).

Edge-computing Vertical Federated Learning

ByCAN: Reverse Engineering Controller Area Network (CAN) Messages from Bit to Byte Level

no code implementations17 Aug 2024 Xiaojie Lin, Baihe Ma, Xu Wang, Guangsheng Yu, Ying He, Ren Ping Liu, Wei Ni

As the primary standard protocol for modern cars, the Controller Area Network (CAN) is a critical research target for automotive cybersecurity threats and autonomous applications.

Template Matching

Fishers Harvest Parallel Unlearning in Inherited Model Networks

no code implementations16 Aug 2024 Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu

A key enabler is the new Unified Model Inheritance Graph (UMIG), which captures the inheritance using a Directed Acyclic Graph (DAG). Central to our framework is the new Fisher Inheritance Unlearning (FIUn) algorithm, which utilizes the Fisher Information Matrix (FIM) from initial unlearning models to pinpoint impacted parameters in inherited models.

model

Channel Twinning: An Enabler for Next-Generation Ubiquitous Wireless Connectivity

no code implementations18 Jun 2024 Yashuai Cao, Jingbo Tan, Jintao Wang, Wei Ni, Ekram Hossain, Dusit Niyato

The emerging concept of channel twinning (CT) has great potential to become a key enabler of ubiquitous connectivity in next-generation (xG) wireless systems.

Scene Recognition

Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learning

1 code implementation17 Jun 2024 Yuanzhe Geng, Erwu Liu, Wei Ni, Rui Wang, Yan Liu, Hao Xu, Chen Cai, Abbas Jamalipour

This paper aims to balance performance and cost in a two-hop wireless cooperative communication network where the source and relays have contradictory optimization goals and make decisions in a distributed manner.

Multi-agent Reinforcement Learning

A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder

1 code implementation2 Jun 2024 Jingjing Zheng, Xin Yuan, Kai Li, Wei Ni, Eduardo Tovar, Jon Crowcroft

The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models.

Federated Learning Model Poisoning

Decentralized Federated Learning Over Imperfect Communication Channels

no code implementations21 May 2024 Weicai Li, Tiejun Lv, Wei Ni, Jingbo Zhao, Ekram Hossain, H. Vincent Poor

With this knowledge, the impact of communication errors can be alleviated, allowing the convergence upper bound to decrease throughout aggregations.

Federated Learning Image Classification

Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments

no code implementations15 May 2024 Pengcheng Sun, Erwu Liu, Wei Ni, Kanglei Yu, Xinyu Qu, Rui Wang, Yanlong Bi, Chuanchun Zhang, Abbas Jamalipour

Non-independent and identically distributed (Non- IID) data adversely affects federated learning (FL) while heterogeneity in communication quality can undermine the reliability of model parameter transmission, potentially degrading wireless FL convergence.

Clustering Federated Learning

FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks

no code implementations23 Apr 2024 Bingnan Xiao, Jingjing Zhang, Wei Ni, Xin Wang

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices.

Federated Learning Scheduling

Privacy at a Price: Exploring its Dual Impact on AI Fairness

no code implementations15 Apr 2024 Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni, David Smith, Thierry Rakotoarivelo

The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.

Fairness

BlockFUL: Enabling Unlearning in Blockchained Federated Learning

no code implementations26 Feb 2024 Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu

Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships.

Federated Learning

Intelligent Reflecting Surfaces and Next Generation Wireless Systems

no code implementations26 Feb 2024 Yashuai Cao, Hetong Wang, Tiejun Lv, Wei Ni

Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2. 0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage.

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

no code implementations6 Feb 2024 Siguo Bi, Xin Yuan, Shuyan Hu, Kai Li, Wei Ni, Ekram Hossain, Xin Wang

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability.

Survey

Reconfigurable Intelligent Surface-Assisted Localization in OFDM Systems with Carrier Frequency Offset and Phase Noise

no code implementations19 Dec 2023 Hanfu Zhang, Erwu Liu, Rui Wang, Wei Ni, Zhe Xing, Yan Liu, Abbas Jamalipour

The localization accuracy of the proposed algorithm is close to the analytical lower bound, with a root mean square error of lower than $\rm 10^{-2} \: m$.

Position

Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey

no code implementations14 Dec 2023 Yichen Wan, Youyang Qu, Wei Ni, Yong Xiang, Longxiang Gao, Ekram Hossain

Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server.

Data Poisoning Federated Learning +1

Detection and Mitigation of Position Spoofing Attacks on Cooperative UAV Swarm Formations

no code implementations6 Dec 2023 Siguo Bi, Kai Li, Shuyan Hu, Wei Ni, Cong Wang, Xin Wang

Detecting spoofing attacks on the positions of unmanned aerial vehicles (UAVs) within a swarm is challenging.

Position

Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach

no code implementations30 Nov 2023 Kai Li, Jingjing Zheng, Xin Yuan, Wei Ni, Ozgur B. Akan, H. Vincent Poor

The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones.

Federated Learning Model Poisoning

OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface

no code implementations25 Nov 2023 Shuyan Hu, Xin Yuan, Wei Ni, Xin Wang, Ekram Hossain, H. Vincent Poor

Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence.

Federated Learning

A Secure Aggregation for Federated Learning on Long-Tailed Data

no code implementations17 Jul 2023 Yanna Jiang, Baihe Ma, Xu Wang, Guangsheng Yu, Caijun Sun, Wei Ni, Ren Ping Liu

As a distributed learning, Federated Learning (FL) faces two challenges: the unbalanced distribution of training data among participants, and the model attack by Byzantine nodes.

Federated Learning

Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities

no code implementations13 Jul 2023 Kai Li, Billy Pik Lik Lau, Xin Yuan, Wei Ni, Mohsen Guizani, Chau Yuen

In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments.

Marketing Mixed Reality +1

Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions

no code implementations3 Jul 2023 Bingnan Xiao, Xichen Yu, Wei Ni, Xin Wang, H. Vincent Poor

The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future.

Federated Learning

Learn to Unlearn: A Survey on Machine Unlearning

no code implementations12 May 2023 Youyang Qu, Xin Yuan, Ming Ding, Wei Ni, Thierry Rakotoarivelo, David Smith

This inspired recent research on removing the influence of specific data samples from a trained ML model.

Fairness Machine Unlearning +1

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

no code implementations8 May 2023 Yanna Jiang, Baihe Ma, Xu Wang, Ping Yu, Guangsheng Yu, Zhe Wang, Wei Ni, Ren Ping Liu

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world.

Federated Learning Management

New Adversarial Image Detection Based on Sentiment Analysis

1 code implementation3 May 2023 Yulong Wang, Tianxiang Li, Shenghong Li, Xin Yuan, Wei Ni

Deep Neural Networks (DNNs) are vulnerable to adversarial examples, while adversarial attack models, e. g., DeepFool, are on the rise and outrunning adversarial example detection techniques.

Adversarial Attack Sentiment Analysis

Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

no code implementations11 Mar 2023 Yulong Wang, Tong Sun, Shenghong Li, Xin Yuan, Wei Ni, Ekram Hossain, H. Vincent Poor

This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models.

Adversarial Attack Adversarial Defense

Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning

no code implementations7 Mar 2023 Xin Yuan, Wei Ni, Ming Ding, Kang Wei, Jun Li, H. Vincent Poor

The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.

Federated Learning Privacy Preserving

Optimal Beamforming for MIMO DFRC Systems with Transmit Covariance Constraints

no code implementations6 Mar 2023 Chenhao Yang, Xin Wang, Wei Ni, Yi Jiang

Under this approach, we reveal that the optimal receive beamforming is given by the classic MMSE one and the optimal transmit beamforming design amounts to solving an orthogonal Procrustes problem, thereby allowing for closed-form solutions to subproblems in each BCD step and fast convergence of the proposed algorithm to a high-quality (near-optimal) overall beamforming design.

Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation

no code implementations13 Feb 2023 Weicai Li, Tiejun Lv, Yashuai Cao, Wei Ni, Mugen Peng

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round.

Federated Learning

Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems

no code implementations10 Feb 2023 Yingping Cui, Tiejun Lv, Wei Ni, Abbas Jamalipour

This paper puts forth a new, reconfigurable intelligent surface (RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT).

RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework

no code implementations10 Feb 2023 Shuyan Hu, Xin Yuan, Wei Ni, Xin Wang, Abbas Jamalipour

This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications.

Deep Reinforcement Learning Trajectory Planning

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

1 code implementation7 Jan 2023 Guangsheng Yu, Xu Wang, Caijun Sun, Qin Wang, Ping Yu, Wei Ni, Ren Ping Liu, Xiwei Xu

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.

Fairness Federated Learning

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

no code implementations7 Nov 2022 Harrison Kurunathan, Hailong Huang, Kai Li, Wei Ni, Ekram Hossain

It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.

Survey

Dispersed Pixel Perturbation-based Imperceptible Backdoor Trigger for Image Classifier Models

no code implementations19 Aug 2022 Yulong Wang, Minghui Zhao, Shenghong Li, Xin Yuan, Wei Ni

In this paper, we propose a new backdoor trigger, which is easy to generate, imperceptible, and highly effective.

Trajectory Planning of Cellular-Connected UAV for Communication-assisted Radar Sensing

no code implementations27 Jul 2022 Shuyan Hu, Xin Yuan, Wei Ni, Xin Wang

Being a key technology for beyond fifth-generation wireless systems, joint communication and radar sensing (JCAS) utilizes the reflections of communication signals to detect foreign objects and deliver situational awareness.

Trajectory Planning

Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning

no code implementations17 Jul 2022 Jingheng Zheng, Hui Tian, Wanli Ni, Wei Ni, Ping Zhang

Under perfect channel state information (CSI), the new framework minimizes the aggregated model's distortion and retains the local models' recoverability by optimizing the transmit beamformers of the devices, the receive beamformers of the BS, and the RIS configuration in an alternating manner.

Federated Learning Robust Design

Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT

1 code implementation15 Feb 2022 Jingjing Zheng, Kai Li, Naram Mhaisen, Wei Ni, Eduardo Tovar, Mohsen Guizani

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT).

Deep Reinforcement Learning Edge-computing +1

Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors

no code implementations3 Nov 2021 Yulong Wang, Shenghong Li, Wei Ni, David Abbott, Mark Johnson, Guangyu Pei, Mark Hedley

We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search.

Combinatorial Optimization

Navigation of a UAV Equipped with a Reconfigurable Intelligent Surface for LoS Wireless Communication with a Ground Vehicle

no code implementations18 Oct 2021 Mohsen Eskandari, Hailong Huang, Andrey V. Savkin, Wei Ni

In this work, we propose an RIS-outfitted UAV (RISoUAV) to secure an uninterrupted line-of-sight (LoS) link with a ground moving target (MT).

Joint Resource Management for MC-NOMA: A Deep Reinforcement Learning Approach

no code implementations29 Mar 2021 Shaoyang Wang, Tiejun Lv, Wei Ni, Norman C. Beaulieu, Y. Jay Guo

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered.

Deep Reinforcement Learning Management +2

Joint Optimization of Trajectory, Propulsion and Thrust Powers for Covert UAV-on-UAV Video Tracking and Surveillance

no code implementations22 Dec 2020 Shuyan Hu, Wei Ni, Xin Wang, Abbas Jamalipour, Dean Ta

Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security.

Tensor-based Multi-dimensional Wideband Channel Estimation for mmWave Hybrid Cylindrical Arrays

no code implementations10 Sep 2020 Zhipeng Lin, Tiejun Lv, Wei Ni, J. Andrew Zhang, Ren Ping Liu

Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications.

Signal Processing

Analysis and Optimization of Service Delay for Multi-quality Videos in Multi-tier Heterogeneous Network with Random Caching

no code implementations21 Jul 2020 Xuewei Zhang, Tiejun Lv, Yuan Ren, Wei Ni, Norman C. Beaulieu

Aiming to minimize service delay, we propose a new random caching scheme in device-to-device (D2D)-assisted heterogeneous network.

Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks

no code implementations20 Jul 2020 Zhipeng Lin, Tiejun Lv, Wei Ni, J. Andrew Zhang, Ren Ping Liu

As a result, only a small number of RF chains are required to preserve the DoF of the UCyA.

Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems

no code implementations19 Jun 2020 Xiaojing Chen, Zhouyu Lu, Wei Ni, Xin Wang, Feng Wang, Shunqing Zhang, Shugong Xu

Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users.

Edge-computing Management

Intelligent Reflecting Surface Aided Multi-User Millimeter-Wave Communications for Coverage Enhancement

no code implementations6 Oct 2019 Yashuai Cao, Tiejun Lv, Wei Ni

Intelligent reflecting surface (IRS) is envisioned as a promising solution for controlling radio propagation environments in future wireless systems.

On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection

no code implementations4 Jun 2019 Kai Li, Wei Ni, Eduardo Tovar

A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e. g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices, while up-to-date knowledge on battery level and data queue of the devices is not available at the UAV.

Deep Reinforcement Learning Q-Learning +3

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