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arXiv 2025年

作者： He, Changpeng Lu, Yang Ai, Bo Dobre, Octavia A. Ding, Zhiguo Niyato, Dusit School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Faculty of Engineering and Applied Science Memorial University St. John’sNLA1C 5S7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated. © 2025, CC BY.

关键词： Graph neural networks

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Jamming Mitigation in JRC Systems via Deep Reinforcement Learning and Backscatter-supported Intelligent Deception Strategy 6

Jamming Mitigation in JRC Systems via Deep Reinforcement Lea...

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6th IEEE International Conference on computer and Communication Systems, ICCCS 2021

作者： Ismail, Lotfi Niyato, Dusit Sun, Sumei Hoang, Dinh Thai Kim, Dong In Liang, Ying-Chang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Institute for Infocomm Research Astar Singapore Singapore School of Electrical and Data Engineering University of Technology Sydney SydneyNSW Australia Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Center for Intelligent Networking and Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9780738126043

In this paper, we develop a framework to optimize the trade-off between radar sensing and data transmission in Joint Radar-Communication (JRC) systems under smart and reactive jamming attacks. First, we propose a novel JRC design architecture that uses backscatter technology and deception strategy to leverage jamming attacks for a JRC system. The deception strategy is used to predict the jammer's action and adopt appropriate counterattack instantaneously, while backscatter technology is used to transmit data on the jamming signals. To deal with the jamming strategy uncertainty (e.g., jamming capability), we then develop a deep reinforcement learning algorithm to quickly find the optimal defense policy for the JRC system. Our in-depth investigation reveals that the proposed design not only significantly undermines the jamming attacks, but also utilises jamming signals to improve the system performances. Compared with conventional anti-jamming methods, our proposed design significantly improves data throughput while maintaining a satisfactory radar sensing performance in dynamic environments. Moreover, the proposed deep reinforcement learning based solution can converge four times faster than a conventional deep Q- Learning based solution. © 2021 IEEE.

关键词： Deep learning

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Rtrl: Relation-Aware Transformer with Reinforcement Learning for Deep Question Generation

SSRN

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SSRN 2024年

作者： Zeng, Hongwei Wei, Bifan Liu, Jun BYD Auto Engineering Research Institute Guandong Shenzhen518118 China School of Computer Sciences and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Continuing Education Xi’an Jiaotong University Shaanxi Xi’an710049 China

Deep question generation is an important yet challenging NLP task where multi-step reasoning chain connecting multiple documents is required to answer the questions. In this paper, we propose a Relation-aware Transformer with Reinforcement Learning (RTRL) to improve the performance of deep question generation. Specifically, RTRL first utilize the relation-aware Transformer to capture both the explicit linguistic and implicit semantic relations for better text representation. Besides, RTRL utilizes the sentence-level reward in a reinforcement learning framework to deal with the metric inconsistency between training and evaluation, and thus improve evaluation metrics with reference questions. Experimental results on HotpotQA dataset shows that RTRL outperforms compared baselines, highlighting its efficiency in generating deep questions. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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CONFOUNDER-AWARE MEDICAL data SELECTION FOR FINE-TUNING PRETRAINED VISION MODELS

arXiv

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arXiv 2025年

作者： Ji, Anyang Kang, Qingbo Xu, Wei Wang, Changfan Li, Kang Lao, Qicheng West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China College of Computer Science Sichuan University Chengdu China School of Biomedical Engineering University of Science and Technology of China Hefei China

The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecting appropriate medical data for downstream fine-tuning remains a significant challenge considering its annotation cost, privacy concerns, and the detrimental effects of confounding variables. In this work, we present a confounder-aware medical data selection approach for medical dataset curation aiming to select minimal representative data by strategically mitigating the undesirable impact of confounding variables while preserving the natural distribution of the dataset. Our approach first identifies confounding variables within data and then develops a distance-based data selection strategy for confounder-aware sampling with a constrained budget in the data size. We validate the superiority of our approach through extensive experiments across diverse medical imaging modalities, highlighting its effectiveness in addressing the substantial impact of confounding variables and enhancing the fine-tuning efficiency in the medical imaging domain, compared to other data selection approaches. Copyright © 2025, The Authors. All rights reserved.

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FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain

arXiv

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arXiv 2023年

作者： Waheed, Nazar Ur Rehman, Ateeq Nehra, Anushka Farooq, Mahnoor Tariq, Nargis Jan, Mian Ahmad Khan, Fazlullah Alalmaie, Abeer Z. Nanda, Priyadarsi School of Electrical and Data Engineering University of Technology Sydney Sydney Australia Department of Information Technology University of Haripur Haripur Pakistan Department of Cs and Engineering Thapar University Patiala Patiala India Department of Computer Engineering Abdul Wali Khan University Mardan Pakistan

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field. © 2023, CC BY.

关键词： Blockchain

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A Hybrid Machine Learning and Deep Learning Approach for Diabetes Prediction With a Tkinter–Based User Interface

A Hybrid Machine Learning and Deep Learning Approach for Dia...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Sai Bhargav Kasetty Issac Neha Margret K. Sai Madhuri Rajesh Kumar Ojha Nuthalapati Sudha K. Rajakumar SCOPE V.I.T University Vellore Tamilnadu India dept. of CSE Data Science BVRIT Narsapur Hyderabad Telangana India dept. of Computer Science & Engineering Silicon University Bhubaneswar Odisha India School of Engineering Mahindra University Hyderabad Telangana India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Diabetes prediction is a critical task in healthcare that can significantly benefit from advancements in machine learning and deep learning technologies. This paper presents a hybrid approach combining deep learning and traditional machine learning models for diabetes prediction. The proposed system integrates a Tkinter-based Graphical User Interface (GUI) to make it easier for users to interact and make predictions. In our discussion, we will cover the design and implementation of the system, and evaluate the performance of different models using precision, F1 score, recall, and, accuracy to provide insights into their effectiveness. Results demonstrate the efficacy of the hybrid approach, with a focus on model accuracy and user experience.

关键词： Deep learning Technological innovation Accuracy Computational modeling Medical services Predictive models User experience Diabetes Informatics Graphical user interfaces

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Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing

arXiv

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arXiv 2022年

作者： Nguyen, Hai M. Chu, Nam H. Nguyen, Diep N. Hoang, Dinh Thai Nguyen, Van-Dinh Hà, Minh Hoàng Dutkiewicz, Eryk Krunz, Marwan The School of Electrical and Data Engineering University of Technology Sydney Australia The College of Engineering and Computer Science VinUniversity Hanoi Viet Nam The ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam The Department of Electrical and Computer Engineering University of Arizona United States

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy while reducing wireless resources. Specifically, an FL learning process can be fused with quantized Binomial mechanism-based updates contributed by multiple users to reduce the communication overhead/cost and to protect the privacy of participating users. However, optimizing quantization parameters, communication resources (e.g., transmit power, bandwidth, and quantization bits), and the added noise to guarantee the differential privacy requirement and performance of the learned FL model remains an open and challenging problem. This article aims to jointly optimize the level of quantization, parameters of the Binomial mechanism, and communication resources so as to maximize the convergence rate under the constraints of the wireless network and differential privacy (DP) requirement. To that end, we first derive a novel DP budget estimation of the FL with quantization and Binomial noise that is tighter than the state-of-the-art bound. We then analyze the relationship between the convergence rate and the transmit power, the bandwidth, the transmission time, and the quantization/noise parameters and provide a theoretical bound on the convergence rate. This theoretical bound is decomposed into two components, including the variance of the global gradient and an upper bound on the quadratic bias that can be minimized by optimizing the communication resources, quantization, and added noise parameters. The resulting optimization turns out to be a Mixed-Integer Non-linear Programming (MINLP) problem. To tackle it, we first transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm to solve the transformed problem with an arbitrary relative error guarantee. Extensive simulations show that under the same wireless resour

关键词： Mobile edge computing

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All in One: Multi-Task Prompting for Graph Neural Networks

arXiv

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arXiv 2023年

作者： Sun, Xiangguo Cheng, Hong Li, Jia Liu, Bo Guan, Jihong Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China School of Computer Science and Engineering Southeast University Purple Mountain Laboratories China Department of Computer Science and Technology Tongji University China

Recently, "pre-training and fine-tuning" has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training pretext often incompatible with these multiple tasks. This gap may even cause a "negative transfer" to the specific application, leading to poor results. Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks. In this paper, we propose a novel multi-task prompting method for graph models. Specifically, we first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. In this way, the prompting idea from NLP can be seamlessly introduced to the graph area. Then, to further narrow the gap between various graph tasks and state-of-the-art pre-training strategies, we further study the task space of various graph applications and reformulate downstream problems to the graph-level task. Afterward, we introduce meta-learning to efficiently learn a better initialization for the multi-task prompt of graphs so that our prompting framework can be more reliable and general for different tasks. We conduct extensive experiments, results from which demonstrate the superiority of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Constructing MDS Array Codes with Small Repair Bandwidth under Sub-packetization Two

Constructing MDS Array Codes with Small Repair Bandwidth und...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Lei Li Chenhao Ying Xinchun Yu Liang Chen Yuanyuan Dong Yuan Luo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Data and Informtion Shenzhen International Graduate School of Tsinghua University Shenzhen China Alibaba Group Hangzhou China

Erasure Codes are widely implemented in distributed storage systems to achieve fault tolerance with high storage efficiency. Reed-Solomon code is commonly deployed in data centers due to its optimal storage efficiency, but it requires massive repair bandwidth for node repair. Minimum Storage Regenerating (MSR) code and Locally Repairable (LR) code are proposed to reduce repair bandwidth. However, MSR code usually carries a heavy disk I/O burden and LR code is not optimal in storage efficiency. In this paper, we take disk I/O, storage efficiency, repair bandwidth, sub-packetization level, and repair degree (number of helper nodes) into consideration together, and propose explicit construction of MDS array code (named LDIO code) with low disk I/O and reduced repair bandwidth, where the sub-packetization level is 2. Besides the low disk I/O consumption, LDIO code also achieves a small repair bandwidth of ${\bar \gamma _{sys}} = \frac{1}{2} + \frac{1}{{4(r - 1)}}$ for systematic nodes with repair degree d = k+1. Normally, LDIO code can provide bandwidth savings of 25% to 50% for the repair of systematic nodes compared with RS code, and almost savings of 6% compared with piggybacking code. For an LDIO code of length n and redundancy r, the size of the finite field needed is Θ(n r+1 ).

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Wireless Resource Optimization in Hybrid Semantic/Bit Communication Networks

arXiv

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arXiv 2024年

作者： Xia, Le Sun, Yao Niyato, Dusit Zhang, Lan Imran, Muhammad Ali The James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

Recently, semantic communication (SemCom) has shown great potential in significant resource savings and efficient information exchanges, thus naturally introducing a novel and practical cellular network paradigm where two modes of SemCom and conventional bit communication (BitCom) coexist. Nevertheless, the involved wireless resource management becomes rather complicated and challenging, given the unique background knowledge matching and time-consuming semantic coding requirements in SemCom. To this end, this paper jointly investigates user association (UA), mode selection (MS), and bandwidth allocation (BA) problems in a hybrid semantic/bit communication network (HSB-Net). Concretely, we first identify a unified performance metric of message throughput for both SemCom and BitCom links. Next, we specially develop a knowledge matching-aware two-stage tandem packet queuing model and theoretically derive the average packet loss ratio and queuing latency. Combined with practical constraints, we then formulate a joint optimization problem for UA, MS, and BA to maximize the overall message throughput of HSB-Net. Afterward, we propose an optimal resource management strategy by utilizing a Lagrange primal-dual transformation method and a preference list-based heuristic algorithm with polynomial-time complexity. Numerical results not only demonstrate the accuracy of our analytical queuing model, but also validate the performance superiority of our proposed strategy compared with different benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

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