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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

IEEE Transactions on Mobile computing 2025年

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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Distill & Contrast: A New Graph Self-Supervised Method With Approximating Nature data Relationships

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IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3284-3297页

作者： He, Dongxiao Zhao, Jitao Guo, Rui Feng, Zhiyong Huo, Cuiying Jin, Di Pedrycz, Witold Zhang, Weixiong Tianjin University College of Intelligence and Computing Tianjin300350 China Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Hong Kong Polytechnic University Department of Data Science and Ai Hong Kong

Contrastive Learning (CL) has emerged as a popular self-supervised representation learning paradigm that has been shown in many applications to perform similarly to traditional supervised learning methods. A key component of CL is mining the latent discriminative relationships between positive and negative samples and using them as self-supervised labels. We argue that this discriminative contrastive task is, in essence, similar to a classification task, and the "either positive or negative"hard label sampling strategies are arbitrary. To solve this problem, we explore ideas from data distillation, which considers probabilistic logit vectors as soft labels to transfer model knowledge. We attempt to abandon the classical hard sampling labels in CL and instead explore self-supervised soft labels. We adopt soft sampling labels that are extracted, without supervision, from the inherent relationships in data pairs to retain more information. We propose a new self-supervised graph learning method, Distill and Contrast (D&C), for learning representations that closely approximate natural data relationships. D&C extracts node similarities from the features and structures to derive soft sampling labels, which also eliminate noise in the data to increase robustness. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method. © 2025 IEEE.

关键词： Self-supervised learning

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Leveraging Graph Neural Networks in Transferring Multimodal Knowledge for Unimodal Segmentation

Leveraging Graph Neural Networks in Transferring Multimodal ...

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IEEE International Symposium on Biomedical Imaging

作者： Tianyi Liu Jiongshu Wang Haochuan Jiang Kaizhu Huang School of Robotics Xi’ an Jiaotong-Liverpool University School of Computing and Mathematical Sciences University of Leicester Data Science Research Center Duke Kunshan University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Accurate segmentation of brain tumors in MRI scans is crucial for effective treatment. Multimodal MRI, including FLAIR, Tlce, T1, and T2 images, provides valuable infor-mation for tumor delineation. However, challenges such data corruption and varying protocols can lead to missing modal-ities, affecting segmentation accuracy. Recent methods at-tempt to transfer knowledge from multimodal teacher mod-els to unimodal student models, but often struggle to capture complex structural relationships within the data. This paper introduces a novel approach called Graph Neural Network-based Knowledge Distillation (GKD), which uses Graph Neu-ral Networks to create a comprehensive graph connecting in-termediate layers of both teacher and student networks. This enables the student network to better learn essential features and their interactions. Evaluations on the BraTS2018 and BraTS2020 datasets demonstrate that GKD significantly en-hances brain tumor segmentation accuracy by deepening the connection between teacher and student insights. The code is publicly available at https://***/T-Y-Liu/GKD.

关键词： Knowledge engineering Image segmentation Accuracy Protocols Codes Magnetic resonance imaging Brain tumors Graph neural networks data models Biomedical imaging

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and Applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics.

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MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation

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Computers in Biology and Medicine 2025年 186卷 109601-109601页

作者： Ke, Xiao Chen, Guanhong Liu, Hao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of data;(ii) the problem of interclass ambiguity caused by motion blur and overexposure to endoscopic light;and (iii) the problem of intraclass inconsistency caused by the variety of morphologies and sizes of the same type of polyps. To address these challenges, we propose a new high-precision polyp segmentation framework, MEFA-Net, which consists of three modules, including the plug-and-play Mask Enhancement Module (MEG), Separable Path Attention Enhancement Module (SPAE), and Dynamic Global Attention Pool Module (DGAP). Specifically, firstly, the MEG module regionally masks the high-energy regions of the environment and polyps through a mask, which guides the model to rely on only a small amount of information to distinguish between polyps and background features, avoiding the model from overfitting the environmental information, and improving the robustness of the model. At the same time, this module can effectively counteract the "dark corner phenomenon" in the dataset and further improve the generalization performance of the model. Next, the SPAE module can effectively alleviate the inter-class fuzzy problem by strengthening the feature expression. Then, the DGAP module solves the intra-class inconsistency problem by extracting the invariance of scale, shape and position. Finally, we propose a new evaluation metric, MultiColoScore, for comprehensively evaluating the segmentation performance of the model on five datasets with different domains. We evaluated the new method quantitatively and qualitatively on five datasets using four metrics. Experimental results show that MEFA-Net significantly improves the accuracy of polyp segmentation and outperforms current state-of-the-art algorithms. Code posted on https://***/

关键词： Endoscopy

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A Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs 12th

A Transformer-Based Spatio-Temporal Graph Neural Network for...

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12th CCF Conference on Bigdata, Bigdata 2024

作者： Gao, Yuanjun Zhu, Quntao Shi, Xuanhua Jin, Hai 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 Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789819610235

Dynamic graphs have emerged as a pivotal data structure underpinning real-world network applications. Against this backdrop, detecting anomalies in dynamic graphs has become particularly important, serving as a foundation for multiple downstream tasks. However, existing research has been limited by only considering the simplistic spatial features of the target edge and being confined to local spatial structures. This confinement imposes significant limitations on model performance. Furthermore, current models have not adequately addressed the issue of missing node attributes, relying solely on one-hot encoding to represent nodes, which restricts the expressive capacity of spatial features. In response to these challenges, we propose an end-to-end transformer-based spatio-temporal graph neural network model called TransSTGNN for detecting anomalous edges on dynamic graphs. This model innovatively integrates the graph convolution network with the transformer model to extract multi-dimensional spatial features of the target edge. Simultaneously, TransSTGNN incorporates a novel node enhancement module that reinforces the representation capability of spatial features by encoding the spatial attributes of nodes, effectively tackling the problem of missing node attributes. Extensive experiments confirm that TransSTGNN surpasses the state-of-the-art methods in anomaly detection on six benchmark datasets and effectively resolves the challenges mentioned above. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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A contrastive learning framework of graph reconstruction and hypergraph learning for key node identification

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Chaos, Solitons and Fractals 2025年 197卷

作者： Huang, Xu-Dong Zhang, Xian-Jie Zhang, Hai-Feng Data Intelligence Lab Research Center For Data To Cyberspace School of Cyber Science and Technology University of Science and Technology of China Hefei Anhui230026 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei Anhui230601 China

With the emergence of complex networks in various domains, the key node identification has become one of the critical issues that needs to be studied. Traditional index methods typically focus on single structural information, while existing data-driven approaches often rely solely on the intrinsic lower-order features of nodes. However, assessing the importance of nodes requires a comprehensive consideration of both network structures and higher-order features from multiple perspectives. To address these challenges, this paper proposes a novel deep learning framework based on Graph Reconstruction and Hypergraph Contrastive Learning, termed GRHCL. The GRHCL method constructs hypergraph structures from original graphs using random walks, followed by leveraging graph reconstruction and hypergraph learning methods to capture both structural and higher-order embedding features of nodes. Positive and negative node pairs are then constructed across different views for contrastive learning. Finally, the model is trained using a training sample set obtained through a clustering sampling strategy, along with a joint loss function. Comparative experiments against various baseline methods demonstrate that the GRHCL method achieves superior predictive performance with smaller training sets, improving accuracy by over 5% on some datasets compared to the next best-performing method. © 2025 Elsevier Ltd

关键词： Clustering sampling Contrastive learning Graph reconstruction learning Hypergraph learning Key node identification

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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IEEE Symposium on High-Performance Computer Architecture

作者： Jiaqi Zhai Xuanhua Shi Kaiyi Huang Chencheng Ye Weifang Hu Bingsheng He Hai Jin 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 Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication.

关键词： Quantization (signal) High dimensional data Memory management Graphics processing units Bandwidth Real-time systems Sparse matrices Recommender systems Field programmable gate arrays Optimization

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Impact of the COVID-19 Pandemic on Operational Performance of Shopping centers:Empirical Evidence from Chinese Retail Stores

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Journal of Systems science and Information 2025年第1期13卷 82-101页

作者： Yongsheng ZHOU Xueqi WANG Xin TIAN Jie SONG School of Business Beijing Technology and Business UniversityBeijing 102488China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing 100190China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190China Shanghai HEADING Information Technology Co. Ltd.Shanghai 200040China

Since the COVID-19 pandemic began in late 2019,economic uncertainty and supply chain disruptions have significantly affected China’s real *** has reduced consumer reliance on physical stores,increasing market and operational risks for brick-and-mortar *** study analyzes the impact of the pandemic on shopping center performance using operational data from 7,010 stores in China alongside COVID-19-related *** analysis focuses on two indicators:Rent levels and lease termination rates,considering both rental and tenant *** reveal that COVID-19 negatively impacts retail rents and lease termination rates,with regions experiencing severe outbreaks facing greater *** lockdowns,the negative effects on rents and termination rates were more pronounced than during the broader pandemic ***,stores with sales-based rent experience a greater adverse impact compared to those *** study concludes that the pandemic significantly reduces store rent while the likelihood of early lease terminations remains relatively *** empirical results offer valuable insights for shopping center operational management and crisis response strategies.

关键词： retail store shopping center operational performance COVID-19 pandemic

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Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

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Jisuanji Yanjiu yu Fazhan/Computer research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

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