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Gradient sparsification for efficient wireless federated learning with differential privacy

Science China(Information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation School of Information and Communication Engineering Hainan University School of Communications and Information Engineering Nanjing University of Posts and Telecommunications School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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Smart Contract-Based Auditing of Edge Data for Vehicular networks

Smart Contract-Based Auditing of Edge Data for Vehicular Net...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yu Zhao Yangguang Tian Chunbo Wang Xiaoqiang Di Hui Qi Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the development of vehicular networks and cloud-edge collaborative technologies, a large amount of vehicle data is collected at edge nodes (Edge Node, EN) for analysis and decision-making. However, edge data faces challenges in terms of integrity and security. Data owners (Data Owner, DO) should delegate auditors to periodically verify the integrity of the data. However, existing verification methods have not yet addressed issues related to verifiers forging evidence and fair payment. This paper proposes a smart contract-based edge data integrity verification scheme. An audit tree based on lattice hashing is designed, allowing the smart contract to initiate multiple verification challenges while only storing a complete label, thus reducing storage overhead. The homomorphic additivity of lattice hashing supports arbitrary data segmentation as challenges, effectively preventing EN from forging evidence. This scheme also designs two smart contract arbitration algorithms to ensure fair payment. Experimental comparisons show that this scheme effectively resolves the trust issues related to EN and ensures fair payment among EN, CSP, and DO.

关键词： Fabrication Cloud computing Data integrity Smart contracts Collaboration Lattices Security Sustainable development Faces Internet of Vehicles

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LAGNet: A Hybrid Deep Learning Model for Automatic Modulation Recognition

LAGNet: A Hybrid Deep Learning Model for Automatic Modulatio...

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IEEE Conference on Wireless Communications and networking

作者： Zhuo Li Guangyue Lu Yuxin Li Hao Zhou Huan Li Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.

关键词： Deep learning Accuracy Neural networks Modulation Pattern classification Feature extraction Reliability

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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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The Entrapment Problem in Random Walk Decentralized Learning

The Entrapment Problem in Random Walk Decentralized Learning

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IEEE International Symposium on Information Theory

作者： Zonghong Liu Salim El Rouayheb Matthew Dwyer Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ USA Network Cyber and Computational Sciences Division DEVCOM Army Research Laboratory Adelphi MD USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper explores decentralized learning in a graph-based setting, where data is distributed across nodes. We investigate a decentralized SGD algorithm that utilizes a random walk to update a global model based on local data. Our focus is on designing the transition probability matrix to speed up convergence. While importance sampling can enhance centralized learning, its decentralized counterpart, using the Metropolis-Hastings (MH) algorithm, can lead to the entrapment problem, where the random walk becomes stuck at certain nodes, slowing convergence. To address this, we propose the Metropolis-Hastings with Levy Jumps (MHLJ) algorithm, which incorporates random perturbations (jumps) to overcome entrapment. We theoretically establish the convergence rate and error gap of MHLJ and validate our findings through numerical experiments.

关键词： Monte Carlo methods Perturbation methods Distributed databases Data models Convergence Information theory

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Scratch Each Other’s Back: Incomplete Multi-modal Brain Tumor Segmentation Via Category Aware Group Self-Support Learning

Scratch Each Other’s Back: Incomplete Multi-modal Brain Tum...

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International Conference on computer Vision (ICCV)

作者： Yansheng Qiu Delin Chen Hongdou Yao Yongchao Xu Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering

Although Magnetic Resonance Imaging (MRI) is very helpful for brain tumor segmentation and discovery, it often lacks some modalities in clinical practice. As a result, degradation of prediction performance is inevitable. According to current implementations, different modalities are considered to be independent and non-interfering with each other during the training process of modal feature extraction, however they are complementary. In this paper, considering the sensitivity of different modalities to diverse tumor regions, we propose a Category Aware Group Self-Support Learning framework, called GSS, to make up for the information deficit among the modalities in the individual modal feature extraction phase. Precisely, within each prediction category, predictions of all modalities form a group, where the prediction with the most extraordinary sensitivity is selected as the group leader. Collaborative efforts between group leaders and members identify the communal learning target with high consistency and certainty. As our minor contribution, we introduce a random mask to reduce the possible biases. GSS adopts the standard training strategy without specific architectural choices and thus can be easily plugged into existing incomplete multi-modal brain tumor segmentation. Remarkably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets demonstrate that GSS can improve the performance of existing SOTA algorithms by 1.27-3.20% in Dice on average. The code is released at https://***/qysgithubopen/GSS.

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information Research Institute.

关键词： Support vector machines

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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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Design and Implementation of Links Generation for Inter Domain Routing System 12

Design and Implementation of Links Generation for Inter Doma...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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