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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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Multimodal Reinforcement Learning with Dynamic Graph Representations for Autonomous Driving Decision-Making 14

Multimodal Reinforcement Learning with Dynamic Graph Represe...

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14th International Conference on Information science and Technology, ICIST 2024

作者： Su, Ting Zhang, Chun-Yang College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350353334

It is crucial for autonomous vehicles to make safe and effective decisions in real-time dynamic road environments through decision-making systems. Traditional rulebased decision-making methods struggle to handle complex and variable traffic conditions, limiting their reliability. data-driven methods such as imitation learning (IL) and reinforcement learning (RL) offer better generalization and adaptability. However, existing methods often fail to capture comprehensive and accurate representations of traffic scenes, especially in hightraffic areas like roundabouts, where neglecting the impact of other traffic participants poses significant safety risks. To address this issue, we propose a multimodal reinforcement learning framework based on integrated dynamic graph representation learning (DGMRL). Our framework designs a spatial-temporalcoupled dynamic graph neural network, which implicitly models temporal information and integrates interaction information and temporal evolution in a unified manner. Additionally, we employ a cross-attention fusion mechanism to effectively integrate multimodal data, constructing a comprehensive driving environment representation. Extensive experimental validation demonstrates that our method outperforms existing baseline models across diverse driving scenarios, with particularly significant performance improvements in traffic-dense environments. © 2024 IEEE.

关键词： Reinforcement learning

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Blockchain-based trust management for data exchange in internet of vehicle network

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Multimedia Tools and Applications 2025年第8期84卷 4837-4855页

作者： Srivastava, Sandeep Agarwal, Deepshikha Chaurasia, Brijesh Kumar Adhikari, Mainak Computer Science Indian Institute of Information Technology UP Lucknow226002 India Information Technology Indian Institute of Information Technology UP Lucknow226002 India Computer Science Pranveer Singh Institute of Technology UP Kanpur208029 India School of Data Science Indian Institute of Science Education & Research Thiruvananthapuram India

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis

Quantum-inspired Language Model with Lindblad Master Equatio...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Yan, Kehuan Lai, Peichao Wang, Yilei College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798891761148

Quantum-inspired models have demonstrated superior performance in many downstream language tasks, such as question answering and sentiment analysis. However, recent models primarily focus on embedding and measurement operations, overlooking the significance of the quantum evolution process. In this work, we present a novel quantum-inspired neural network, LI-QiLM, which integrates the Lindblad Master Equation (LME) to model the evolution process and the interferometry to the measurement process, providing more physical meaning to strengthen the interpretability. We conduct comprehensive experiments on six sentiment analysis datasets. Compared to the traditional neural networks, transformer-based pre-trained models and quantum-inspired models, such as CICWE-QNN and ComplexQNN, the proposed method demonstrates superior performance in accuracy and F1-score on six commonly used datasets for sentiment analysis. Additional ablation tests verify the effectiveness of LME and interferometry. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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EEG Individual Identification Using Multi-Dimensional Attention Graph Convolutional Network 17

EEG Individual Identification Using Multi-Dimensional Attent...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Lin, Xing Zeng, Hongxi Huang, Zhihua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798331507398

Currently, the field of individual identification utilizing coded modulation visual evoked potentials (cVEP) is gaining significant attention. However, existing methods face challenges due to the EEG signals' low signal-to-noise ratio and inter-session variability among participants. To address these issues, we introduce a Multi-Dimensional Attention Graph Convolutional Network (MDAGCN) tailored for identifying cVEP biomarkers in personal identification tasks. Our approach first employs a Temporal-Attention Enhanced Convolutional Neural Network (TEEGNet) to extract time-frequency domain features, which are subsequently integrated with functional connectivity and spatial distance matrices to construct functional connectivity and spatial distance brain networks. Subsequently, a spatial attention mechanism coupled with graph convolution is utilized to extract deep features from these networks. The MDAGCN model is optimized by minimizing the categorical cross-entropy loss, ensuring an optimal fit to the data. Experimental evaluation on a cVEP EEG dataset comprising 20 subjects validates the model's efficacy, achieving a cross-session recognition accuracy of 72.17%, surpassing benchmark methods. Our novel MDAGCN framework presents a promising avenue for cVEP biomarker individual identification. © 2024 IEEE.

关键词： Frequency domain analysis

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CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models

CONTESTS: a Framework for Consistency Testing of Span Probab...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wagner, Eitan Slavutsky, Yuli Abend, Omri School of Computer Science and Engineering Department of Statistics and Data Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9798891761643

Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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Research on Embedding Intelligent Models in EEG data Platform 17

Research on Embedding Intelligent Models in EEG Data Platfor...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Wu, Qikai Lin, Binghui Cheng, Ye Huang, ZhiHua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798331507398

With the rapid development of artificial intelligence (AI) technology, its application in the field of clinical electroen-cephalography (EEG) diagnosis shows remarkable prospects. It has become an urgent need to assist medical experts in clinical EEG diagnosis practice by constructing EEG intelligent models to automatically identify and diagnose EEG signals. This process involves the differences of AI algorithms and the complexity for practical applications. Therefore, the key lies in how to embed the EEG intelligent models into the EEG data platform of practical applications, manage these intelligent models in a unified and standardised way, and enhance their practicality and effectiveness in clinical EEG diagnosis. To this end, we have designed and developed an EEG data platform application that aims to provide services for the embedding of intelligent models and provide a standardised interface in terms of intelligent algorithms. Users can register algorithms according to their own needs, construct models on the platform in combination with EEG datasets to achieve the embedding of intelligent models, and provide convenient services for the unified management of models. This platform provides conditions for standardising the process of embedding EEG intelligent models and supports the auxiliary application of intelligent models in clinical diagnosis. © 2024 IEEE.

关键词： Embeddings

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Multi-View Image Tampering Detection and Localization in Real Scene Based on Spatial-Channel Attention 15

Multi-View Image Tampering Detection and Localization in Rea...

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15th International Conference on Graphics and Image Processing, ICGIP 2023

作者： Liu, Hanquan Zhong, Shangping Chen, Kaizhi College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9781510675018

To cope with the threat of image content tampering in real scenes, this paper develops a multi-view spatial-channel attention network (MSCA-Net), which can use multi-view features and multi-scale features to detect whether an image has been tampered with and predict tampered regions. By introducing the frequency domain view of the image, the model can use the noise distribution around the tampered region to learn semantically independent features and detect subtle tampering traces that are difficult to detect in the RGB domain. Secondly, a new Efficient Spatial-Channel Attention Module (ESCM) is proposed to capture the correlation between different channels and between global pixels. MSCA-Net improves the localization performance of tampered regions on real-scene images by generating segmentation masks step by step at multiple scales through a progressive guidance mechanism. MSCA-Net runs very fast and is capable of processing 1080P resolution images at 40FPS+. Extensive experimental results demonstrate the promising performance of MSCA-Net on both image-level and pixel-level tampering detection tasks. © 2024 SPIE. All rights reserved.

关键词： Pixels

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Ince-EEGNet: A Novel Fatigue Degree Detection Method Based on EEG 17

Ince-EEGNet: A Novel Fatigue Degree Detection Method Based o...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Ke, Jingshuang Gao, Ruochen Huang, Zhihua College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798331507398

In this paper, we proposed a convolutional neural network based on EEGNet, which can be used to classify the degree of fatigue, without manual feature extraction, and directly take the original EEG as input, and the results of five-fold cross-validation show that the performance of the network is better than that of shallowConvNet and EEGNet, with the highest accuracy of 98.02%. Using EEG samples of different frequency bands as model inputs, it was found that the sample accuracy of gamma rhythm and beta rhythm was higher, both exceeding 90 %, and the accuracy of delta rhythm and theta rhythm was lower, only about 50 %. In addition, the frontal lobe had the highest classification accuracy in all brain regions, reaching nearly 94%, and the left brain had an accuracy of about 4 % higher than that of the right brain. © 2024 IEEE.

关键词： Convolutional neural networks

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