no code implementations • 10 Apr 2024 • Wanting Yang, Zehui Xiong, Yanli Yuan, Wenchao Jiang, Tony Q. S. Quek, Merouane Debbah
In the era of 6G, featuring compelling visions of intelligent transportation system, digital twins, remote surveillance is poised to become a ubiquitous practice.
no code implementations • 10 Apr 2024 • Yiru Wang, Wanting Yang, Zehui Xiong, Yuping Zhao, Tony Q. S. Quek, Zhu Han
Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges.
no code implementations • 20 Mar 2024 • Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Han Hu, Hangguan Shan, Tony Q. S. Quek
This paper deals with federated learning (FL) in the presence of malicious Byzantine attacks and data heterogeneity.
no code implementations • 11 Mar 2024 • Chenhao Wang, Zihan Chen, Nikolaos Pappas, Howard H. Yang, Tony Q. S. Quek, H. Vincent Poor
In contrast, an Adam-like algorithm converges at the $\mathcal{O}( 1/T )$ rate, demonstrating its advantage in expediting the model training process.
no code implementations • 10 Feb 2024 • Yongqing Xu, Yong Li, Tony Q. S. Quek
Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks.
1 code implementation • NeurIPS 2023 • Zihan Chen, Howard H. Yang, Tony Q. S. Quek, Kai Fong Ernest Chong
Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously.
no code implementations • 25 Jan 2024 • Xi Song, Lu Yuan, Zhibo Qu, Fuhui Zhou, Qihui Wu, Tony Q. S. Quek, Rose Qingyang Hu
Unmanned aerial vehicles (UAVs) are widely used for object detection.
no code implementations • 4 Jan 2024 • Chuanhong Liu, Caili Guo, Yang Yang, Wanli Ni, Tony Q. S. Quek
Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance.
1 code implementation • 1 Dec 2023 • Xingqiu He, Chaoqun You, Tony Q. S. Quek
In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used.
no code implementations • 30 Oct 2023 • Yiwei Li, Chien-Wei Huang, Shuai Wang, Chong-Yung Chi, Tony Q. S. Quek
Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients' data.
no code implementations • 26 Oct 2023 • Xiang Chen, Zhiheng Guo, Xijun Wang, Howard H. Yang, Chenyuan Feng, Junshen Su, Sihui Zheng, Tony Q. S. Quek
Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI).
no code implementations • 6 Oct 2023 • Yanwu Lu, Howard Yang, Nikolaos Pappas, Giovanni Geraci, Chuan Ma, Tony Q. S. Quek
Our work fills a gap in the literature by providing a comprehensive analysis of AoI in NTN and offers new insights into the performance of LEO satellite networks.
no code implementations • 6 Oct 2023 • Zihan Chen, Howard H. Yang, Y. C. Tay, Kai Fong Ernest Chong, Tony Q. S. Quek
Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications.
no code implementations • 4 Oct 2023 • Jingheng Zheng, Wanli Ni, Hui Tian, Deniz Gunduz, Tony Q. S. Quek, Zhu Han
To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL.
no code implementations • 10 Aug 2023 • Feng Wang, Giovanni Geraci, Tony Q. S. Quek
Non-terrestrial networks (NTN) offer potential for efficient content broadcast in remote regions, thereby extending the reach of digital services.
no code implementations • 4 Aug 2023 • Rui Ding, Fuhui Zhou, Yuben Qu, Chao Dong, Qihui Wu, Tony Q. S. Quek
Unmanned aerial vehicle (UAV) communication is of crucial importance for diverse practical applications.
no code implementations • 17 Jun 2023 • Zihan Chen, Howard H. Yang, Tony Q. S. Quek
Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks, but the limited spectral resources often constrain its scalability.
no code implementations • 12 May 2023 • Guoshun Nan, Zhichun Li, Jinli Zhai, Qimei Cui, Gong Chen, Xin Du, Xuefei Zhang, Xiaofeng Tao, Zhu Han, Tony Q. S. Quek
We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare.
no code implementations • 8 May 2023 • Yuwen Cao, Tomoaki Ohtsuki, Setareh Maghsudi, Tony Q. S. Quek
In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS).
no code implementations • 30 Mar 2023 • Yuxuan Zhang, Chao Xu, Howard H. Yang, Xijun Wang, Tony Q. S. Quek
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue.
no code implementations • 23 Mar 2023 • Chaoqun You, Kun Guo, Gang Feng, Peng Yang, Tony Q. S. Quek
With the obtained FL hyperparameters and resource allocation, we design a MAML-based FL algorithm, called Automated Federated Learning (AutoFL), that is able to conduct fast adaptation and convergence.
no code implementations • 22 Mar 2023 • Chuanhong Liu, Caili Guo, Yang Yang, Wanli Ni, Yanquan Zhou, Lei LI, Tony Q. S. Quek
In this paper, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently.
no code implementations • 19 Mar 2023 • Chaoqun You, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks.
no code implementations • 3 Mar 2023 • Yongqing Xu, Yong Li, J. Andrew Zhang, Marco Di Renzo, Tony Q. S. Quek
However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge.
no code implementations • 24 Feb 2023 • Zihan Chen, Zeshen Li, Howard H. Yang, Tony Q. S. Quek
Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue.
no code implementations • 9 Feb 2023 • Shuying Gan, Marie Siew, Chao Xu, Tony Q. S. Quek
Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications.
no code implementations • 3 Dec 2022 • Shuai Wang, Yanqing Xu, Zhiguo Wang, Tsung-Hui Chang, Tony Q. S. Quek, Defeng Sun
In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm.
no code implementations • 28 Sep 2022 • Marie Siew, Shikhar Sharma, Zekai Li, Kun Guo, Chao Xu, Tania Lorido-Botran, Tony Q. S. Quek, Carlee Joe-Wong
In edge computing, users' service profiles are migrated due to user mobility.
no code implementations • 27 Sep 2022 • Chaoqun You, Daquan Feng, Kun Guo, Howard H. Yang, Tony Q. S. Quek
Experimental results verify the effectiveness of PerFedS2 in saving training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.
no code implementations • 30 Jun 2022 • Zihan Chen, Songshang Liu, Hualiang Wang, Howard H. Yang, Tony Q. S. Quek, Zuozhu Liu
Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks.
1 code implementation • CVPR 2022 • Jingyi Xu, Zihan Chen, Tony Q. S. Quek, Kai Fong Ernest Chong
Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL.
no code implementations • 17 Feb 2022 • Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data.
no code implementations • 13 Nov 2021 • Peng Yang, Xianbin Cao, Tony Q. S. Quek, Dapeng Oliver Wu
Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs.
no code implementations • 20 Oct 2021 • Shengheng Liu, Chong Zheng, Yongming Huang, Tony Q. S. Quek
In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks.
no code implementations • 15 Oct 2021 • Zhengchuan Chen, Yifan Feng, Chundie Feng, Liang Liang, Yunjian Jia, Tony Q. S. Quek
Associated with multi-packet reception at the access point, irregular repetition slotted ALOHA (IRSA) holds a great potential in improving the access capacity of massive machine type communication systems.
no code implementations • 20 Aug 2021 • Chenyuan Feng, Howard H. Yang, Deshun Hu, Zhiwei Zhao, Tony Q. S. Quek, Geyong Min
Finally, we provide experiments to evaluate the learning performance of HFL and our MACFL.
no code implementations • 12 Aug 2021 • Zihan Chen, Kai Fong Ernest Chong, Tony Q. S. Quek
Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients.
no code implementations • 9 Aug 2021 • Mao V. Ngo, Tie Luo, Tony Q. S. Quek
In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset, and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
no code implementations • 15 Jun 2021 • Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek
The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.
no code implementations • 3 Jun 2021 • Peng Yang, Tony Q. S. Quek, Jingxuan Chen, Chaoqun You, Xianbin Cao
This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users.
1 code implementation • 27 May 2021 • Jingyi Xu, Tony Q. S. Quek, Kai Fong Ernest Chong
In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as "positive", while the remaining noisy subset is treated as "unlabeled".
Ranked #7 on Image Classification on Clothing1M (using clean data) (using extra training data)
no code implementations • 5 May 2021 • An Liu, Rui Yang, Tony Q. S. Quek, Min-Jian Zhao
Then we propose a PDD-based stochastic successive convex approximation (PDD-SSCA) algorithmic framework to find KKT solutions for two-stage stochastic optimization problems.
no code implementations • 14 Apr 2021 • Seongjun Kim, Minsu Kim, Jong Yeol Ryu, Jemin Lee, Tony Q. S. Quek
By considering the antenna tilt angle-based channel gain, we derive the network outage probability for both IS-BS and ES-BS schemes, and show the existence of the optimal tilt angle that minimizes the network outage probability after analyzing the conflict impact of the antenna tilt angle.
no code implementations • 13 Apr 2021 • Chao Xu, Yiping Xie, Xijun Wang, Howard H. Yang, Dusit Niyato, Tony Q. S. Quek
cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
no code implementations • 5 Mar 2021 • Yanli Yuan, De Wen Soh, Xiao Yang, Kun Guo, Tony Q. S. Quek
Theoretically, we provide a theoretical analysis of the proposed graph estimator, which establishes a non-asymptotic bound of the estimation error under the high-dimensional setting and reflects the effect of several key factors on the convergence rate of our algorithm.
no code implementations • 25 Feb 2021 • Peng Yang, Kun Guo, Xing Xi, Tony Q. S. Quek, Xianbin Cao, Chenxi Liu
Particularly, we first propose to decompose the sequential decision problem into multiple repeated optimization subproblems via a Lyapunov technique.
Networking and Internet Architecture Signal Processing
no code implementations • 14 Nov 2020 • Peng Yang, Xing Xi, Tony Q. S. Quek, Xianbin Cao, Jingxuan Chen
This problem is challenging to be solved due to the requirement of analytically tractable channel models and the non-convex characteristic as well.
1 code implementation • 5 Jul 2020 • Wenchao Xia, Tony Q. S. Quek, Kun Guo, Wanli Wen, Howard H. Yang, Hongbo Zhu
In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels.
no code implementations • 15 Apr 2020 • Mao V. Ngo, Tie Luo, Hakima Chaouchi, Tony Q. S. Quek
We build an HEC testbed, implement our proposed approach, and evaluate it using real IoT datasets.
no code implementations • 10 Jan 2020 • Mao V. Ngo, Hakima Chaouchi, Tie Luo, Tony Q. S. Quek
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
no code implementations • 1 Nov 2019 • Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farokhi Farhad, Shi Jin, Tony Q. S. Quek, H. Vincent Poor
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
no code implementations • 31 Oct 2019 • Howard H. Yang, Ahmed Arafa, Tony Q. S. Quek, H. Vincent Poor
Federated learning (FL) is a machine learning model that preserves data privacy in the training process.
Information Theory Signal Processing Information Theory
no code implementations • 26 Oct 2019 • Hoon Lee, Tony Q. S. Quek, Sang Hyun Lee
For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods.
no code implementations • 24 Sep 2019 • Khai Nguyen Doan, Mojtaba Vaezi, Wonjae Shin, H. Vincent Poor, Hyundong Shin, Tony Q. S. Quek
To address the power allocation problem, two novel methods are proposed.
no code implementations • 14 Sep 2019 • Chuan Ma, Jun Li, Ming Ding, Howard Hao Yang, Feng Shu, Tony Q. S. Quek, H. Vincent Poor
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).
Networking and Internet Architecture
no code implementations • 17 Aug 2019 • Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek, H. Vincent Poor
Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.
Information Theory Signal Processing Information Theory
no code implementations • 31 May 2019 • Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek
This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations.
no code implementations • 13 Dec 2018 • Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek, Inkyu Lee
Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate.
no code implementations • NIPS Workshop CDNNRIA 2018 • Yuxin Zhang, Huan Wang, Yang Luo, Lu Yu, Haoji Hu, Hangguan Shan, Tony Q. S. Quek
Despite enjoying extensive applications in video analysis, three-dimensional convolutional neural networks (3D CNNs)are restricted by their massive computation and storage consumption.
no code implementations • 21 Nov 2017 • Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks.