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Controlling update distance and enhancing fair trainable prototypes in federated learning under data and model heterogeneity

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Defence technology(防务技术) 2025年第5期47卷 15-31页

作者： Kangning Yin Zhen Ding Xinhui Ji Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Institute of Public Security Kash Institute of Electronics and Information IndustryKash 844000China Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center Shanghai 200241China

Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat *** prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model *** methods allow for the sharing of only class representatives between heterogeneous clients while maintaining ***,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization *** address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous ***,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive *** experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.

关键词： Heterogeneous federated learning Model heterogeneity Data heterogeneity Contrastive learning

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Privacy-Enhanced Data Sharing in Multi-Institutional Healthcare 9

Privacy-Enhanced Data Sharing in Multi-Institutional Healthc...

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9th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2024

作者： Xiao, Jixiang Zeng, Shengke Zhou, Tiantian Feng, Yawen School of Computer and Software Engineering Xihua University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350373554

In multi-institutional patient data sharing scenarios, maintaining fine-grained access control while safeguarding privacy and adapting to real-world environments is crucial. Traditional attribute-based encryption (ABE) schemes may inadvertently expose sensitive information through access policies and lack support for dynamic policy updates. To tackle these challenges, this paper proposes an ABE scheme with partially hidden access policies to bolster privacy protection for encrypted data stored on servers across healthcare institutions. Our scheme also facilitates access policy updates, ensuring accurate and efficient adaptation to evolving real-world needs. Through rigorous security analysis, we validate the efficacy of our approach in safeguarding patient data integrity, confidentiality, and adaptability to dynamic environments. Our scheme offers a robust solution for secure and efficient multi-institutional patient data sharing, supporting fine-grained access control, privacy preservation, and dynamic policy updates. © 2024 IEEE.

关键词： Access control

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A novel image encryption algorithm based on new one-dimensional chaos and DNA coding

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Multimedia Tools and Applications 2024年第36期83卷 84275-84297页

作者： Feng, Sijia Zhao, Maochang Liu, Zhaobin Li, Yuanyu Tianjin public emergency early warning information release center Tianjin Meteorology Information Center Tianjin300074 China School of Information Science & amp Technology Dalian Maritime University Dalian116026 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

This paper introduces a new one-dimensional chaotic system and a new image encryption algorithm. Firstly, the new chaotic system is analyzed. The bifurcation diagram and Lyapunov exponent show that the system has strong chaotic characteristics and is suitable for the field of image encryption. The chaotic sequence generated by the system is used in image encryption, scrambled according to its sequence value, and several sequences required for DNA coding operation are generated. The encrypted image is obtained by encoding, decoding, and diffusion operation according to the sequence. The initial values and parameters of system are generated by Hash algorithm based on plain image, so it has strong sensitivity and can effectively resist attacks such as selective plaintext attack. Experimental results show that the algorithm has high security and robustness, and can resist common attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Chaotic systems

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Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

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Computers, Materials & Continua 2024年第10期81卷 685-706页

作者： Yali Si Feng Li Shan Zhong Chenghang Huo Jing Chen Jinglian Liu School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China Suzhou Industrial Intelligence and Big Data Laboratory Suzhou215000China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang524088China

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning ***,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive *** better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal ***,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time ***,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active *** further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,*** experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation *** specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

关键词： Location-based social networks adaptive successive point-of-interest recommendation long short-term preference trajectory sequences

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Feature Preserving Parameterization for Quadrilateral Mesh Generation Based on Ricci Flow and Cross Field

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Computer Modeling in engineering & sciences 2023年第10期137卷 843-857页

作者： Na Lei Ping Zhang Xiaopeng Zheng Yiming Zhu Zhongxuan Luo International School of Information Science and Engineering Dalian University of TechnologyDalian116000China School of Mathematical Sciences Dalian University of TechnologyDalian116000China School of Software Technology Dalian University of TechnologyDalian116000China

We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature *** the field of quadrilateral generation with features,the cross field methods are wellknown because of their superior performance in feature *** methods based on metrics are popular due to their sound theoretical basis,especially the Ricci flow *** cross field methods’major part,the Poisson equation,is challenging to solve in three dimensions *** it comes to cases with a large number of elements,the computational costs are expensive while the methods based on metrics are on the *** addition,an appropriate initial value plays a positive role in the solution of the Poisson equation,and this initial value can be obtained from the Ricci flow *** we combine the methods based on metric with the cross field *** use the discrete dynamic Ricci flow algorithm to generate an initial value for the Poisson equation,which speeds up the solution of the equation and ensures the convergence of the *** experiments show that our method is effective in generating a quadrilateral mesh for models with features,and the quality of the quadrilateral mesh is reliable.

关键词： Quadrilateral mesh feature preserving Ricci flow cross field

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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Image Super-Resolution Reconstruction by Lightweight Convolution Network 4

Image Super-Resolution Reconstruction by Lightweight Convolu...

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4th International Symposium on Electrical, electronics and information engineering, ISEEIE 2024

作者： Liu, Renting Ren, Chunhui Fu, Yusheng Fu, Miaomiao Information Center University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350355772

Image super-resolution reconstruction is widely adopted for improving the resolution of low-resolution images and enhancing its visual effects. In this paper, we propose a Horizontal and Vertical Super-resolution Reconstruction algorithm (HVSR) based on a lightweight convolutional neural network. By designing a novel horizontal and vertical asymmetric convolution kernel instead of traditional symmetric ones, we achieve a reasonable balance between model complexity and reconstruction performance and improve its computational efficiency. Meanwhile, we also refine the feature extraction sub-network and introduce skip connections and group convolution to further enhance feature extraction capabilities. Comprehensive analysis of experimental comparison shows that, compared with the state-of-the-art algorithms based on fast model and deep network (DN), the proposed HVSR can achieve a higher peak signal-to-noise ratio (SNR), share a comparable reconstruction speed as the fast-model-based ones, and be much faster than the DN-based ones. Moreover, with a structural similarity comparable to the existing DN-based algorithms, the proposed HVSR can beat the existing fast-model-based ones in terms of super-resolution visual effects. © 2024 IEEE.

关键词： Convolutional neural networks

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Temporal Convolutional Network for Speech Bandwidth Extension

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China Communications 2023年第11期20卷 142-150页

作者： Chundong Xu Cheng Zhu Xianpeng Ling Dongwen Ying School of Information Engineering Jiangxi University of Science and TechnologyJiangxi 341000China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model *** the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the *** order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the *** addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency *** experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.

关键词： speech bandwidth extension temporal convolutional networks time-frequency loss

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Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification

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IEEE Wireless Communications Letters 2025年第6期14卷 1728-1732页

作者： Pan, Rui Chen, Hui Shen, Guanxiong Chen, Hongyang University of Electronic Science and Technology of China School of Information and Communication Engineering P. R. Chengdu611731 China Southeast University School of Cyber Science and Engineering Nanjing China Zhejiang Lab China

In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework. By applying least square (LS) and minimum mean square error (MMSE) channel estimations followed by equalization, signals with different residual channel effects are generated. These residual channels enable the model to learn more effective representations. Then the pre-trained model is fine-tuned with 1% samples in a novel environment for RFFI. Experimental results demonstrate that our method significantly enhances both feature extraction ability and generalization while requiring fewer samples and less time, making it suitable for practical wireless security applications. © 2012 IEEE.

关键词： Contrastive Learning

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