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Incentive Mechanism Design for Semi-Asynchronous Federated Learning Based on Contract Theory: A Learning Approach

IEEE Internet of Things Journal 2025年

作者： Yao, Yuan Li, Gang Chen, Hongbin Chen, Hongming Inner Mongolia University College of Computer Science Hohhot 010021 China Guilin University of Electronic Technology School of Information and Communication Guilin 541004 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan 316022 China

Semi-Asynchronous Federated Learning (SAFL) leverages the benefits of synchronous and asynchronous updates, effectively addressing the straggling effect caused by heterogeneity among clients. However, most research focuses on synchronous FL, overlooking the incentive mechanisms crucial for active participation in SAFL. Moreover, client-specific private information including data quality, computational resources, and privacy preferences is multi-dimensional and inaccessible to the server, yet essential for effective decision-making. To address these challenges, we propose a novel SAFL framework incorporating a learning-based contract with the consideration of multi-dimensional private information. Specifically, we integrate model staleness and data quality into the aggregated weight design in the proposed SAFL. Then, we formulate a server utility maximization problem to optimize local iterations and reward allocation for different client types, ensuring theoretical guarantees of convergence, individual rationality (IR), and incentive compatibility (IC). Extensive simulations on real-world datasets demonstrate that our approach significantly enhances global accuracy and convergence speed compared to existing works on aggregation and contract design methods. © 2014 IEEE.

关键词： deep learning Federated learning incentive mechanism multi-dimensional private information

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SSHPool: The Separated Subgraph-based Hierarchical Pooling

arXiv

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arXiv 2024年

作者： Xu, Zhuo Cui, Lixin Li, Ming Wang, Yue Lyu, Ziyu Du, Hangyuan Bai, Lu Yu, Philip S. Hancock, Edwin R. School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China The Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China School of Cyber Science and Technology Sun Yat-Sen University Guangzhou China School of Computer and Information Technology Shanxi University Taiyuan China Department of Computer Science University of Illinois at Chicago United States Department of Computer Science University of York York United Kingdom

In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ the local graph convolution units as the local structure to further compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. Since these subgraphs are separated by different clusters and the structural information cannot be propagated between them, the local convolution operation can significantly avoid the over-smoothing problem caused by message passing through edges in most existing Graph Neural Networks (GNNs). By hierarchically performing the proposed procedures on the resulting coarsened graph, the proposed SSHPool can effectively extract the hierarchical global features of the original graph structure, encapsulating rich intrinsic structural characteristics. Furthermore, we develop an end-to-end GNN framework associated with the SSHPool module for graph classification. Experimental results demonstrate the superior performance of the proposed model on real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Efficient and Privacy-Enhancing Non-Interactive Periocular Authentication for Access Control Services

引用

IEEE Transactions on Network and Service Management 2025年

作者： Li, Yating Wang, Le Xue, Liang Liu, Jingwei Lin, Xiaodong Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Guelph School of Computer Science GuelphN1G 2W1 Canada York University School of Information Technology North YorkM3J 1P3 Canada

Periocular authentication has emerged as an increasingly prominent approach in access control services, especially in situations of face occlusion. However, its limited feature area and low information complexity make it susceptible to tampering and identity forgery. To address these vulnerabilities, we propose a practical privacy-enhancing non-interactive periocular authentication scheme for access control services. It enables encrypted cosine similarity matching on an untrusted remote authentication server by leveraging random masking (RM) and dual-trapdoor homomorphic encryption (DTHE). Additionally, a weight control matrix is introduced to enhance authentication accuracy by assigning importance to different feature dimensions. To accommodate devices with varying trust levels, we employ adaptive authentication strategies. For trusted mobile devices, we implement secure single-factor authentication based on periocular features, while for external devices with unknown security status, we enforce a two-factor authentication mechanism integrating periocular features with tokens to mitigate unauthorized access risks. Additionally, our scheme conceals users’ true identities from external devices during authentication. Security analysis demonstrates that our solution effectively mitigates tampering and replay attacks in the network while preventing privacy leakage. As validated by experimental results, the proposed scheme enables efficient authentication of obscured faces. © 2004-2012 IEEE.

关键词： Differential privacy

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Core-periphery Detection Based on Masked Bayesian Non-negative Matrix Factorization

arXiv

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arXiv 2024年

作者： Wang, Zhonghao Yuan, Ru Fu, Jiaye Wong, Ka-Chun Peng, Chengbin College of Information Science and Engineering Ningbo University Ningbo315200 China Key Laboratory of Mobile Network Application Technology of Zhejiang Province Ningbo315200 China Department of Computer Science City University of Hong Kong 999077 Hong Kong School of Management Xi’an Polytechnic University Xi’an710048 China

Core-periphery structure is an essential mesoscale feature in complex networks. Previous researches mostly focus on discriminative approaches while in this work, we propose a generative model called masked Bayesian non-negative matrix factorization. We build the model using two pair affiliation matrices to indicate core-periphery pair associattions and using a mask matrix to highlight connections to core nodes. We propose an approach to infer the model parameters, and prove the convergence of variables with our approach. Besides the abilities as traditional approaches, it is able to identify core scores with overlapping core-periphery pairs. We verify the effectiveness of our method using randomly generated networks and real-world networks. Experimental results demonstrate that the proposed method outperforms traditional approaches. © 2024, CC BY.

关键词： Complex networks

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Research on Ornamental Fish Recognition Base on Improved Shufflenet V2 Network

Research on Ornamental Fish Recognition Base on Improved Shu...

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Xingyun Zhang Hongming Chen Longqing Zhang Qian Chen Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Computer Science Guangdong University of Science and Technology Dongguan China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the advancement of computer vision technology, fish recognition plays a crucial role in various fields such as aquaculture processing and disseminating fish knowledge. In this study, we propose an enhanced and lightweight fish recognition algorithm to accomplish fish classification recognition with limited data samples. Based on the ShuffleNet V2 network, we incorporate Transfer Learning and the SE attention mechanism. Additionally, we replace the ReLu activation function with the AM-Softmax activation function. Comparative evaluations are conducted against Resnet50, MobileNet V2, MobileNet V3, and the original ShuffleNet V2 network model. Our experimental findings demonstrate that the proposed algorithm outperforms the aforementioned models, resulting in recognition accuracy improvements of 16.01%, 19.05%, 15.04%, and 14.44% respectively. The final recognition accuracy achieved is 97.20%, successfully attaining the desired outcome.

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Detection Data Characteristics and Recognition of Tunnel Fissure Water

SSRN

引用

SSRN 2023年

作者： Li, Chuan Wang, Shiyi Wang, Yunsheng Zhang, Panpan Yang, Xi Wan, Xiaorong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Yunnan Aerospace Engineering Geophysical Detecting co.ltd Yunnan Kunming650500 China

Tunnel fissure water may induce geological disasters of mud bursting and collapse during the construction stage. Therefore, it is important to automatically extract and identify fissure water in front of the tunnel face for tunnel construction. The paper performs diffraction migration imaging of seismic data after P/S wave separation, and the imaging results reflect the three-dimensional distribution of undesirable geologic bodies. Second, based on the geologic bodies obtained above, ground-penetrating radar detection is added to identify alternative area of fissure water at the peak point of the A-scan. The boundary point of the alternative area of fissure water is located at the peak point where the phase of the reflected wave and the incident wave are reversed. Finally, the area with a larger amplitude and smaller frequency than the surrounding rock is screened as the target area among the alternative area, and the distribution of fissure water is labeled in the B-scan. The method is applied to the mileage section K38+440$\sim$+320 of Liangwangshan Tunnel in Yunnan, and the forecast results show that the rock bodies near the mileage section K38+440$\sim$+410, K38+410$\sim$+380, and K38+350$\sim$+320 is more fragmented, the fissure water is developed, and the target area is verified by excavation. © 2023, The Authors. All rights reserved.

关键词： Ground penetrating radar systems

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HC-GAE: the hierarchical cluster-based graph auto-encoder for graph representation learning 24

HC-GAE: the hierarchical cluster-based graph auto-encoder fo...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lu Bai Zhuo Xu Lixin Cui Ming Li Yue Wang Edwin R. Hancock School of Artificial Intelligence Beijing Normal University Beijing China and Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Institute of Optoelectronicss Jinhua China and Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)9798331314385

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets.

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EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAGING

EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAG...

引用

2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Wang, Kuan Chen, Bingxu Li, Ning Guo, Zhengwei Fu, Zewen College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible. © 2021 IEEE

关键词： Synthetic aperture radar

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Large-Scale OD Matrix Estimation with A Deep Learning Method

arXiv

引用

arXiv 2023年

作者： Xiong, Zheli Lian, Defu Chen, Enhong Chen, Gang Cheng, Xiaomin The School of Data Science University of Science and Technology of China Anhui Hefei230000 China The Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China The Yangtze River Delta Information Intelligence Innovation Research Institute China

The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS). It involves adjusting an initial OD matrix by regressing the current observations like traffic counts of road sections (e.g., using least squares). However, the OD estimation problem lacks sufficient constraints and is mathematically underdetermined. To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints. However, this approach is highly dependent on the existing prior matrix, which may be outdated. Others add structural constraints through sensor data, such as vehicle trajectory and speed, which can reflect more current structural constraints in real-time. Our proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. This approach combines the advantages of both deep learning and numerical optimization algorithms. The neural network(NN) learns to infer structural constraints from probe traffic flows, eliminating dependence on prior information and providing real-time performance. Additionally, due to the generalization capability of NN, this method is economical in engineering. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset. Subsequently, we verified the stability of our method on real traffic data. Our experiments provided confirmation of the benefits of combining NN and numerical *** Codes I.2.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Statistical tests

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A 7nm-Based Decodable Self-Resetting Regfile Circuit

A 7nm-Based Decodable Self-Resetting Regfile Circuit

引用

International Conference on ASIC

作者： Wanlong Zhao Yuejun Zhang Mingze Ren Liang Wen Pengjun Wang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo University Ningbo China Department of Electronic Technology China Coast Guard Academy Ningbo China College of Electrical and Electronic Engineering Wenzhou University Wenzhou China

This paper presents a novel design scheme for a decodable self-resetting Regfile circuit that aims to reduce the power consumption caused by massive data and high computing power. The scheme consists of three steps: first, it analyzes the working principle and the energy consumption process of the decoding circuit; second, it proposes a self-resetting decoding circuit structure that uses the clock to control the decoding state and automatically resets when the clock is low, thus avoiding the extra power consumption due to the inversion of the memory cell state during the non-working period of the decoding circuit; third, it implements a two-read, two-write, decodable self-resetting Regfile circuit using a full-custom design method under the TSMC 7nm FinFET process. The experimental results demonstrate that the circuit has an area of 5593μm 2 and an energy-delay product of 0.41pJ•nS, and it can operate stably at 4GHz under various temperatures and process corner conditions.

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