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RobustPFL: Robust Personalized Federated Learning

IEEE Transactions on Dependable and Secure computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Mengying Xu Yunxiao Zu Jie Zhou Jiapeng Dai Jing Chen Meiru Liu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Department of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

Flying ad hoc networks (FANETs) are composed of multiple UA V nodes that are distributed across a wide area and linked via wireless communication to gather and transmit environmental data. However, the battery-powered UAVs in FANETs imposes significant energy limitations, thereby shortening the network's operational lifespan. This paper proposes an energy-efficient clustering protocol based on improved hybrid grey wolf optimization (EEC-IHGWO), which can optimally select cluster heads (CHs), significantly extend network lifetime and maximize energy efficiency. The selection criteria include factors like remaining energy, the distance from cluster members to CHs, and node density. The performance of the EEC-IHGWO method, measured by network lifetime, the number of living nodes, and remaining energy, is assessed against traditional algorithms such as LEACH, as well as the more recent PSO and FCM. The simulation results demonstrate that EEC-IHGWO sustains a longer network lifetime and higher energy efficiency.

关键词： Wireless communication Protocols Machine learning algorithms Simulation Clustering algorithms Energy efficiency Ad hoc networks Magnetic heads Particle swarm optimization Optimization

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

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Multimedia Systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the Medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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EeCA: A Novel Approach for Energy Conservation in MEC via NDN-Based Content Caching

EeCA: A Novel Approach for Energy Conservation in MEC via ND...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Jiaxin Xu Huiling Shi Haoxiang Chu Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

In the context of escalating demands on wireless networks owing to the widespread application of 5G and the Internet of Things (IoT), enhancing network performance and user experience through the convergence of Multi-access Edge computing (MEC) and Information-Centric networking (ICN) has become a critical research focus. This paper proposes a novel Energy-efficient Caching Algorithm (EeCA) based on Named Data networking (NDN), a specific implementation of ICN. This NDN-based caching strategy extends local caching in MEC networks and enhances the overall network performance by considering the multitude of repeated computational tasks and the residual energy of low-energy devices. The EeCA aims to reduce redundant offloading of tasks across the entire network, contributing to energy savings, and reducing overall system costs. Simulations reveal superior performance of EeCA compared to four other caching methods, specifically in terms of cache hit ratio and energy consumption, making it a promising solution for network performance enhancement and energy conservation in MEC networks.

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Social Internet of Tings Trust Management Based on Implicit Social Relationship 5th

Social Internet of Tings Trust Management Based on Implicit ...

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5th International Conference on security and Privacy in New computing Environments, SPNCE 2022

作者： Zhang, Hongbin Fan, Fan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Jian School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

The "Social Internet of Things (SIoT)" is a combination of the Internet of Things (IoT) and social networks to form a new paradigm. The SIoT promotes the development of smart cities, smart transportation, and many other fields. In SIoT, the openness and mobility of objects are enhanced. However, this tends to lead to network data sparsity problems. By distinguishing explicit and implicit social relationships, we introduce an implicit social relationship-based trust management model (IRTM) for reliable service delivery in SIoT. IRTM establishes implicit social relationships among nodes by mining their latent characteristics and trust transitivity. It models SIoT by creating sub-networks for each social relationship as a way to fuse the impact of different types of social relationships on trust management. To address the problem of malicious attacks by malicious nodes in the network to protect their interests, it considers two metrics, node relationship strength, and recommendation reliability, to filter malicious recommendations. Experiments conducted in the presence of data sparsity and malicious objects show that IRTM can improve the accuracy and convergence of trust evaluation compared to other methods that ignore implicit social relationships when computing trust. In addition, our scheme can improve resistance to trust-related attacks. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Optimizing Resource Utilization in Consumer Electronics networks Through an Enhanced Grey Wolf Optimization Algorithm With UAV Collaboration

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IEEE Transactions on Consumer Electronics 2025年

作者： Yang, Xiaoteng Feng, Jie Liu, Lei Pei, Qingqi Xidian University School of Telecommunications Engineering Xi’an 710071 China Xidian University Guangzhou Institute of Technology Guangzhou 510555 China Xidian University State Key Laboratory of Integrated Services Network the Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an 710071 China

With the continuous advancement of electronic technology, consumer electronic devices (CEs) have become indispensable to people’s daily lives. However, with the increasing complexity of device functions and the rapid growth of data size, efficiently utilizing resources and improving computing performance has become an important issue that needs to be urgently addressed in consumer electronic networks. Existing research often finds it difficult to effectively balance global optimization and local fine-tuning when facing dynamic and complex environments, resulting in poor resource scheduling efficiency and, to a certain extent, restricting the system’s scalability. To address these problems, this paper proposes an adaptive enhanced grey wolf optimization(AEGWO) algorithm that integrates the skyhawk hunting strategy to improve the collaborative computing capabilities of the UAV network. The algorithm introduces a new search mechanism, a nonlinear convergence factor. It combines the individual optimal learning method to more effectively balance global exploration and local optimization, thereby improving resource scheduling efficiency and the system’s adaptability. Simulation experimental results show that AEGWO performs better than existing algorithms on standard test functions and achieves significant resource optimization effects in UAV-assisted computing scenarios. These results verify the effectiveness of AEGWO in improving system efficiency and optimizing resource allocation and provide a new solution for electronic device resource management in complex computing environments. © 1975-2011 IEEE.

关键词： collaborative computing Consumer electronics grey wolf optimization unmanned aerial vehicles

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On Covert Communication Performance with Outdated CSI in Wireless Greedy Relay Systems

On Covert Communication Performance with Outdated CSI in Wir...

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作者： Bai, Jiaqing He, Ji Chen, Yanping Shen, Yulong Jiang, Xiaohong Future University Hakodate School of System Information Science Hakodate041-8655 Japan School of Computer Science and Technology Xidian University Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China Department of Computer Science and Technology Xi'An University of Posts and Telecommunications Xi'an710121 China

Communication performance relies largely on the availability of channel state information (CSI). This paper investigates the impact of outdated CSI on the achievable covert communication performance in a two-hop wireless relay system under two typical covert transmission schemes of rate-control transmission (RCT) and power-control transmission (PCT). We first apply the typical channel feedback delay model to determine the statistical distribution of outdated CSI, based on which we then develop theoretical models to depict the inherent relationship between outdated CSI and the fundamental covert performance metrics in terms of detection error probability (DEP) and covert rate (CR). With the help of these models, we further explore the optimization of DEP subject to the CR constraint as well as the optimization of CR subject to the DEP constraint to reveal the max-min DEP performance and the maximal CR performance with the outdated CSI. Finally, extensive numerical results are provided to illustrate the impact of outdated CSI on the covert communication performance. © 2005-2012 IEEE.

关键词： Power control

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