no code implementations • 2 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.
no code implementations • 19 Dec 2024 • Qimei Cui, Xiaohu You, Wei Ni, Guoshun Nan, Xuefei Zhang, Jianhua Zhang, Xinchen Lyu, Ming Ai, Xiaofeng Tao, Zhiyong Feng, Ping Zhang, Qingqing Wu, Meixia Tao, Yongming Huang, Chongwen Huang, Guangyi Liu, Chenghui Peng, Zhiwen Pan, Tao Sun, Dusit Niyato, Tao Chen, Muhammad Khurram Khan, Abbas Jamalipour, Mohsen Guizani, Chau Yuen
The first stage, AI for Network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences.
no code implementations • 4 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.
no code implementations • 2 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).
no code implementations • 22 Aug 2024 • Yousef Emami, Luis Almeida, Kai Li, Wei Ni, Zhu Han
Ethical principles must be embedded in AVs to align their behavior with societal values and norms.
no code implementations • 17 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.
no code implementations • 16 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.
no code implementations • 21 Jun 2024 • Xiaojing Chen, Zhenyuan Li, Wei Ni, Xin Wang, Shunqing Zhang, Yanzan Sun, Shugong Xu, Qingqi Pei
Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data.
no code implementations • 18 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.
1 code implementation • 17 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.
1 code implementation • 2 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.
no code implementations • 21 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.
no code implementations • 15 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.
no code implementations • 24 Apr 2024 • Xin Jin, Tiejun Lv, Wei Ni, Zhipeng Lin, Qiuming Zhu, Ekram Hossain, H. Vincent Poor
Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks.
1 code implementation • 23 Apr 2024 • Kai Li, Xin Yuan, Jingjing Zheng, Wei Ni, Falko Dressler, Abbas Jamalipour
This paper puts forth a new training data-untethered model poisoning (MP) attack on federated learning (FL).
no code implementations • 23 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.
no code implementations • 15 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.
no code implementations • 13 Mar 2024 • Minyu Shen, Weihua Gu, Michael J. Cassidy, Yongjie Lin, Wei Ni
We show that this damaging cycle can be abated in simple ways.
no code implementations • 26 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.
no code implementations • 26 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.
no code implementations • 6 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.
no code implementations • 19 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 $\rm 10^{-2} \: m$.
no code implementations • 14 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.
no code implementations • 6 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.
no code implementations • 30 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.
no code implementations • 25 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.
no code implementations • 17 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.
no code implementations • 13 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.
no code implementations • 3 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.
no code implementations • 12 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.
no code implementations • 8 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.
1 code implementation • 3 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.
no code implementations • 11 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.
no code implementations • 7 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.
no code implementations • 6 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.
no code implementations • 13 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.
no code implementations • 10 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).
no code implementations • 10 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.
1 code implementation • 7 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.
no code implementations • 7 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.
no code implementations • 19 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.
no code implementations • 27 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.
no code implementations • 17 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.
no code implementations • 24 Jun 2022 • Shaoyang Wang, Chau Yuen, Wei Ni, Guan Yong Liang, Tiejun Lv
Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask.
1 code implementation • 15 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).
no code implementations • 3 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.
no code implementations • 18 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).
no code implementations • 29 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.
no code implementations • 22 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.
no code implementations • 10 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
no code implementations • 21 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.
no code implementations • 20 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.
no code implementations • 19 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.
no code implementations • 20 Dec 2019 • Yashuai Cao, Tiejun Lv, Zhipeng Lin, Wei Ni
Millimeter-wave (mmWave) communications provide access to spectra with bandwidths and in abundance.
no code implementations • 6 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.
no code implementations • 4 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.