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arXiv 2025年

作者： Wang, Jingyuan Lin, Yujing Li, Yudong School of Computer Science and Engineering Beihang University Beijing China MIIT Key Laboratory of Data Intelligence and Management Beihang University Beijing China School of Economics and Management Beihang University Beijing China

Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods rely on the global road network structure of cities. When the road network structure changes, these methods are often not transferable to other cities. In fact, there exist invariant mobility patterns between different cities: 1) People prefer paths with the minimal travel cost;2) The travel cost of roads has an invariant relationship with the topological features of the road network. Based on the above insight, this paper proposes a Generalizable Trajectory Generation model (GTG). The model consists of three parts: 1) Extracting city-invariant road representation based on Space Syntax method;2) Cross-city travel cost prediction through disentangled adversarial training;3) Travel preference learning by shortest path search and preference update. By learning invariant movement patterns, the model is capable of generating trajectories in new cities. Experiments on three datasets demonstrates that our model significantly outperforms existing models in terms of generalization ability. Copyright © 2025, The Authors. All rights reserved.

关键词： data privacy

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Deep Diffusion Gradients Leakage in Federated Learning

Deep Diffusion Gradients Leakage in Federated Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dexuan Chen Yueyi Luo Qianqian Qi Hongxiao Fei Big Data Institute Central South University Changsha China School of Mathematics and Statistics Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In federated learning (FL), multiple clients train a global model by sharing gradients. Since the client data remains locally, federated learning is considered privacy-safe. Although previous studies have demonstrated the feasibility of recovering client data from shared gradients, most studies assume that there is no privacy defense in the federated setting. In this work, we propose Deep Diffusion Gradients Leakage, which uses the prior information of the diffusion model to compensate for the gradient information degradation caused by privacy defense, and reconstructs high-quality original images from deep neural networks under privacy defense. We also propose a step-by-step optimization strategy to solve the gradient matching problem in the process of reconstructing the image by sequentially optimizing the latent variables of the diffusion model, and more finely control the reconstruction results. We hope that our method can promote the development of privacy-preserving methods for federated learning.

关键词： Privacy Federated learning Image synthesis Process control Signal processing Diffusion models Speech processing Protection Image reconstruction Optimization

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Integrated Interpolation and Matrix Completion for Radio Map Estimation: A Convex Optimization Approach

Integrated Interpolation and Matrix Completion for Radio Map...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongcheng Dong Wenqiang Pu Rui Zhou Xiao Fu Feng Yin Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Radio map estimation (RME) is crucial for effective planning and optimization of wireless networks. Traditional approaches such as interpolation excel at capturing local smoothness in densely populated data but struggle with sparse or irregular data. Conversely, matrix completion (MC) approaches utilize global structures but require huge number of samples and may produce non-smooth estimates. To integrate these strengths, we propose a convex optimization approach for RME (IIMC-RME) that merges interpolation with MC. This approach formulates the RME task as a low-rank MC problem constrained by interpolated results. Additionally, we have developed a convergent algorithm utilizing the alternating direction method of multipliers (ADMM) to efficiently solve the IIMC-RME problem. Experimental evaluations on both synthetic and real-world datasets have shown that IIMC-RME surpasses existing approaches, thereby achieving superior accuracy in RME.

关键词： Interpolation Wireless networks Estimation Signal processing algorithms Signal processing Convex functions Planning Sparse matrices Speech processing Optimization

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What factors influence research impact?An empirical study on the interplay of research,publications,researchers,institutions,and national conditions

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Journal of data and Information Science 2025年第1期10卷 188-227页

作者： Mudassar Hassan Arsalan Omar Mubin Abdullah Al Mahmud Sajida Perveen School of Computer Data and Mathematical SciencesWestern Sydney UniversityNew South Wales 2116Australia Centre for Design Innovation Swinburne University of TechnologyVictoria 3122Australia School of Civil Engineering and Built Environment Queensland University of TechnologyQueensland 4001Australia

Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a global survey of 630 scientists across diverse disciplines,genders,regions,and experience levels,Structural Equation Modelling(SEM)was employed to assess the influence of 29 factors related to researcher characteristics,research attributes,publication strategies,institutional support,and national ***:The study validated the Quintuple Helix Model,uncovering complex *** support significantly affects research impact by covering leadership,resources,recognition,and *** attributes,including academic experience and domain knowledge,also play a crucial *** socioeconomic conditions indirectly influence research impact by supporting institutions,underscoring the importance of conducive national *** limitations:While the study offers valuable insights,it has *** statistically sufficient,the response rate was below 10%,suggesting that the findings may not fully represent the entire global research *** reliance on self-reported data may also introduce bias,as perceptions of impact can be *** implications:The findings have a significant impact on researchers aiming to enhance their work’s societal,economic,and cultural significance,institutions seeking supportive environments,and policymakers interested in creating favourable national conditions for impactful *** study advocates for a strategic alignment among national policies,institutional practices,and individual researcher efforts to maximise research impact and effectively address global ***/value:By empirically validating the Research Impact Quintuple Helix Model,this study offers a holistic framework for understanding the synergy of factors that drive impactful

关键词： Research impact Quintuple Helix Model Structural Equation Modelling(SEM) Global survey Institutional support Socioeconomic conditions

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Advances and Open Challenges in Federated Foundation Models

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IEEE Communications Surveys and Tutorials 2025年

作者： Ren, Chao Yu, Han Peng, Hongyi Tang, Xiaoli Zhao, Bo Yi, Liping Tan, Alysa Ziying Gao, Yulan Li, Anran Li, Xiaoxiao Li, Zengxiang Yang, Qiang School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden College of Computing and Data Science Nanyang Technological University Singapore College of Computer Nankai University China School of Medicine Yale University United States Digital Research Institute of Enn Group Langfang China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong WeBank Shenzhen China

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field. © 2025 IEEE.

关键词： Federated learning

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FedVC: Virtual Clients for Federated Autonomous Driving With Imbalanced Label Distribution

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhang, Yongquan Chen, Hongda Wang, Zhihui Kumar, Rahul Wang, Changmiao Zhejiang University of Finance and Economics School of Data Science Hangzhou310018 China Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266510 China Ssv Pg College Department Of Mathematics Hapur245101 India Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning in autonomous driving safeguards the privacy of individual vehicles during collaborative training by avoiding the exchange of raw data. These vehicles often suffer from imbalanced label distribution, making the federated learning model developed from them critical for safety. Most current solutions addressing label imbalance in federated learning are oriented towards classification tasks. However, there is limited research focused on prediction tasks involving imbalanced datasets relevant to autonomous driving. To address the identified gap, we introduce an innovative peer-to-peer federated learning framework called FedVC. This framework includes the use of virtual clients to enhance the ability of the collaborative training process to recognize the global data distribution effectively. It strategically samples relevant segments of local data from various clients for each training round and controls the execution time for backpropagation associated with each virtual client. Importantly, FedVC constructs a global perspective by utilizing metadata from each client, thereby maintaining data privacy while overcoming the challenge of label imbalance in autonomous driving prediction tasks, all without the need to share any raw datasets. Experimental results demonstrate that FedVC outperforms classical FedAvg and the most recent methods at three steering angle prediction datasets with different levels of imbalanced label distribution. The source code is publicly available at https://***/aikedaerC/FedVC. © 1975-2011 IEEE.

关键词： Autonomous Driving data Imbalance Federated Learning Virtual Clients

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Covert Communication in RSMA-Assisted Ambient Backscatter Communication Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Zhuo Yang, Liang Lei, Hongjiang Li, Xingwang Niyato, Dusit Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering China Jishou University School of Communication and Electronic Engineering China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering The Chongqing Key Laboratory of Mobile Communications Technology Chongqing400065 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

In this paper, we investigate covert communication in rate-splitting multiple access (RSMA)-assisted ambient backscatter communication (AmBC) systems. Specifically, in the presence of illegitimate users, a base station applies RSMA to the downlink transmission of covert users and common users in the the primary system of ambient backscatter. To evaluate the covertness of the system, we derive closed-form expressions for the detection error probability (DEP), outage probability (OP), and covert rate (CR), where the DEP is applied to evaluate the ability of the warden to detect the presence or absence of communication behaviors between the base station and the covert user. Based on these expressions, we also conduct an asymptotic analysis of the outage probability, an upper bound analysis of the covert rate, and an analysis of the impact of the power allocation factor on system performance. Numerical results show that the RSMA scheme has a higher covert rate and a higher minimum DEP value compared to the non-orthogonal multiple access (NOMA) scheme, but it may reduce its outage performance. Additionally, an appropriate use of the power allocation factor can enhance the covertness of the system. Furthermore, we find that the co-channel interference between the primary and secondary systems in the ambient backscatter system can degrade the covert performance of the primary system. © 2002-2012 IEEE.

关键词： Backscattering

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C2AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Wu, Wenxuan Chen, Xueyuan Wang, Shuai Wang, Jiadong Meng, Lingwei Wu, Xixin Meng, Helen Li, Haizhou The Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong Nanjing University School of Intelligence Science and Technology Suzhou215163 China The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data Shenzhen518172 China Technical University of Munich Germany The Chinese University of Hon g Kong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore

Audio-Visual Target Speaker Extraction (AV-TSE) aims to mimic the human ability to enhance auditory perception using visual cues. Although numerous models have been proposed recently, most of them estimate target signals by primarily relying on local dependencies within acoustic features, underutilizing the human-like capacity to infer unclear parts of speech through contextual information. This limitation results in not only suboptimal performance but also inconsistent extraction quality across the utterance, with some segments exhibiting poor quality or inadequate suppression of interfering speakers. To close this gap, we propose a model-agnostic strategy called the Mask-And-Recover (MAR). It integrates both inter- and intra-modality contextual correlations to enable global inference within extraction modules. Additionally, to better target challenging parts within each sample, we introduce a Fine-grained Confidence Score (FCS) model to assess extraction quality and guide extraction modules to emphasize improvement on low-quality segments. To validate the effectiveness of our proposed model-agnostic training paradigm, six popular AV-TSE backbones were adopted for evaluation on the VoxCeleb2 dataset, demonstrating consistent performance improvements across various metrics. © 2025 IEEE.

关键词： Self-supervised learning

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AI-Enhanced Resource Allocation for LPWAN-based LoRaWAN:A Hybrid TinyML and Deep Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Lodhi, Muhammad Ali Sun, Xiaobing Mahmood, Khalid Lodhi, Anum Park, Youngho Hussain, Majid Yangzhou University School of Information Engineering Yangzhou China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of computer science and engineering China Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of The University of Faisalabad Department of Computer Science Faisalabad38850 Pakistan

The integration of Artificial Intelligence (AI) with Low Power Wide Area Networks (LPWAN) offers a promising approach to address resource constraints and dynamic network conditions inherent in these networks. However, deploying complex AI algorithms on resource-limited edge devices presents significant challenges due to their limited computational capabilities. In this study, we propose a hybrid Tiny Machine Learning (TinyML) and Deep Neural Network (DNN)-based solution for optimizing resource allocation in LPWAN-based LoRaWAN networks, targeting both static and mobile applications. Our approach leverages the strengths of a 1-D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model implemented on the network server, combined with TinyML models deployed on edge devices. The CNN-LSTM model predicts optimal spreading factor and transmission power by analyzing spatial and temporal patterns from real-time data, while the TinyML models enable edge devices to autonomously adjust communication parameters in resource-constrained and disconnected scenarios. This hybrid framework enhances network performance by improving the packet success ratio (PSR), maximizing energy efficiency, and addressing the challenges posed by dynamic IoT environments. © 2014 IEEE.

关键词： Deep neural networks

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