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Game-Theoretic Incentive Mechanism for Blockchain-Based Federated Learning

IEEE Transactions on Mobile computing 2025年

作者： Tang, Wenzheng Liu, Erwu Ni, Wei Qu, Xinyu Huang, Butian Li, Kezhi Niyato, Dusit Jamalipour, Abbas Tongji University College of Electronics and Information Engineering Shanghai201804 China Tongji University Department of Ophthalmology Tongji Hospital School of Medicine China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China University College London Institute of Health Informatics LondonWC1E 6BT United Kingdom College of Computing and Data Science 639798 Singapore University of Sydney School of Electrical and Computer Engineering NSW2006 Australia

Blockchain-based federated learning (BFL) has gained attention for its potential to establish decentralized trust. While existing research primarily focuses on personalized frameworks for various applications, essential aspects including incentive mechanisms—critical for ensuring stable system operation—remain under-explored. To bridge this gap, we propose a game-theoretic incentive mechanism designed to foster active participation in BFL tasks. Specifically, we model a BFL system comprising a model owner (MO), i.e., task publisher, multiple miners, and training terminals, framing their interactions through two-tier Stackelberg games. In the first-tier game, the MO designs reward strategies to incentivize training terminals to contribute more data, enhancing model accuracy. The second-tier game introduces a multi-leader multi-follower Stackelberg game, enabling miners to set model packaging prices based on competitors' strategies and anticipated user behavior. By deriving the Stackelberg equilibrium, we identify optimal strategies for all participants, leading to an incentive mechanism balancing individual interests with overall performance. Compared to its benchmarks, our incentive mechanism offers 5.8% and 53.4% higher utilities in the two games compared to its alternatives, accelerating convergence and improving accuracy. © 2002-2012 IEEE.

关键词： Federated learning

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Balanced Multi-view Clustering

arXiv

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arXiv 2025年

作者： Li, Zhenglai Wang, Jun Tang, Chang Zhu, Xinzhong Zhang, Wei Liu, Xinwang Faculty of data science City University of Macau 999078 China School of computer National University of Defense Technology Changsha410073 China School of computer China University of Geosciences Wuhan430074 China College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua China Research Institute of Ningbo Cixing Co. Ltd Ningbo China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250000 China

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets. The demo code of this work is publicly available at https://***/guanyuezhen/BMvC. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Logical entanglement distribution between distant 2D array qubits

arXiv

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arXiv 2025年

作者： Maeda, Yuya Suzuki, Yasunari Kobayashi, Toshiki Yamamoto, Takashi Tokunaga, Yuuki Fujii, Keisuke Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan Center for Quantum Computing RIKEN Wako Saitama351-0198 Japan

Sharing logical entangled pairs between distant quantum nodes is a key process to achieve fault-tolerant quantum computation and communication. However, there is a gap between current experimental specifications and theoretical requirements for sharing logical entangled states while improving experimental techniques. Here, we propose an efficient logical entanglement distribution protocol based on surface codes for two distant 2D qubit array with nearest-neighbor interaction. A notable feature of our protocol is that it allows post-selection according to error estimations, which provides the tunability between the infidelity of logical entanglements and the success probability of the protocol. With this feature, the fidelity of encoded logical entangled states can be improved by sacrificing success rates. We numerically evaluated the performance of our protocol and the trade-off relationship, and found that our protocol enables us to prepare logical entangled states while improving fidelity in feasible experimental parameters. We also discuss a possible physical implementation using neutral atom arrays to show the feasibility of our protocol. © 2025, CC BY.

关键词： Qubits

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ACSNet: A Deep Neural Network for Compound GNSS Jamming Signal Classification

arXiv

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arXiv 2025年

作者： Jiang, Min Ye, Ziqiang Xiao, Yue Gao, Yulan Xiao, Ming Niyato, Dusit Chengdu611731 China Division of Information Science and Engineering KTH Royal Institute of Technology Stockholm100 44 Sweden College of Computing and Data Science Nanyang Technological University Singapore

In the global navigation satellite system (GNSS), identifying not only single but also compound jamming signals is crucial for ensuring reliable navigation and positioning, particularly in future wireless communication scenarios such as the space-air-ground integrated network (SAGIN). However, conventional techniques often struggle with low recognition accuracy and high computational complexity, especially under low jamming-to-noise ratio (JNR) conditions. To overcome the challenge of accurately identifying compound jamming signals embedded within GNSS signals, we propose ACSNet, a novel convolutional neural network designed specifically for this purpose. Unlike traditional methods that tend to exhibit lower accuracy and higher computational demands, particularly in low JNR environments, ACSNet addresses these issues by integrating asymmetric convolution blocks, which enhance its sensitivity to subtle signal variations. Simulations demonstrate that ACSNet significantly improves accuracy in low JNR regions and shows robust resilience to power ratio (PR) variations, confirming its effectiveness and efficiency for practical GNSS interference management applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Global positioning system

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LMP-GAN: Out-Of-Distribution Detection For Non-Control data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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A deep reinforcement learning approach for wind speed forecasting

引用

Engineering Applications of Computational Fluid Mechanics 2025年第1期19卷

作者： Band, Shahab S. Lin, Ting Jia Qasem, Sultan Noman Ameri, Rasoul Shahmirzadi, Danyal Aslam, Muhammad Shamrooz Pai, Hao-Ting Salwana, Ely Mousavi, Amir International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu Taiwan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Institute of the Information Society Ludovika University of Public Service Budapest Hungary John Von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transf... 详细信息

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence deep reinforcement learning empirical wavelet transform long short-term memory machine learning Renewable energy wind speed forecasting

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering 2nd

scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell ...

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wu, Zheyu Wang, Yueyue Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9789819619061

Single-cell RNA sequencing provides high-throughput gene expression information and offers higher resolution of cell differences compared to bulk RNA sequencing, which helps identify cell types and explore cellular heterogeneity at the single-cell level. Although many single-cell clustering methods based on masked autoencoders have been developed recently, they rarely effectively utilize the correlations between cells, resulting in suboptimal clustering results. In this work, we propose a novel clustering method for scRNA-seq data based on a masked graph convolutional autoencoder, which incorporates a graph convolutional network onto the basis of a masked autoencoder to enhance the capture of the correlation between cells and genes by fusing the cell-cell interaction graph and the gene expression matrix. By increasing the relationships between cells, dimensionality reduction is guided, and a clustering-friendly representation is obtained. We conducted extensive comparative experiments using various clustering evaluation metrics on six scRNA-seq datasets from different sequencing platforms. The experimental results demonstrate that scMGCAE outperforms other methods on these datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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Analytic model reveals local molecular polarizability changes induced by collective strong coupling in optical cavities

引用

Physical Review Research 2025年第1期7卷 013242-013242页

作者： Jacob Horak Dominik Sidler Thomas Schnappinger Wei-Ming Huang Michael Ruggenthaler Angel Rubio Max Planck Institute for the Structure and Dynamics of Matter and Center for Free-Electron Laser Science Luruper Chaussee 149 22761 Hamburg Germany The Hamburg Center for Ultrafast Imaging Luruper Chaussee 149 22761 Hamburg Germany PSI Center for Scientific Computing Theory and Data 5232 Villigen PSI Switzerland Department of Physics Stockholm University AlbaNova University Center SE-106 91 Stockholm Sweden Department of Physics and Center for Quantum Information Science National Cheng Kung University Tainan 70101 Taiwan Center for Computational Quantum Physics (CCQ) The Flatiron Institute 162 Fifth Avenue New York New York 10010 USA

Despite recent numerical evidence, one of the fundamental theoretical mysteries of polaritonic chemistry is how and if collective strong coupling can induce local changes of the electronic structure to modify chemical properties. Here we present nonperturbative analytic results for a model system consisting of an ensemble of N harmonic molecules under vibrational strong coupling (VSC) that alters our present understanding of this fundamental question. By applying the cavity Born-Oppenheimer partitioning on the Pauli-Fierz Hamiltonian in dipole approximation, the dressed many-molecule problem can be solved nonperturbatively and analytically in the dilute limit, i.e., a self-consistent solution with the mean-field Hartree-product wave function becomes exact. We discover that the electronic molecular polarizabilities are modified even in the case of vanishingly small single-molecule couplings. Consequently, this nonperturbative local polarization mechanism persists even in the large-N limit. In contrast, a perturbative calculation of the polarizabilities based on the uncoupled ensemble wave function leads to a qualitatively erroneous scaling behavior with vanishing effects in the large-N limit. Nevertheless, the exact (self-consistent) polarizabilities can be determined from single-molecule strong coupling simulations instead. Our fundamental theoretical observations demonstrate that hitherto existing collective-scaling arguments are insufficient for polaritonic chemistry and they pave the way for refined single- (or few-)molecule strong-coupling ab initio simulations of chemical systems under collective strong coupling.

关键词： Hamiltonians

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

引用

arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Implementing termination analysis on quantum programming

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science China(information sciences) 2019年第12期62卷 215-222页

作者： Shusen LIU Kan HE Runyao DUAN School of Data and Computer Science Sun Yat-sen University Institute for Quantum Computing Baidu Beijing Institute for Advanced Study Tsinghua University College of Mathematics & College of Information and Computer Science Taiyuan University of Technology

Termination analysis is an essential part in programming. Especially quantum programming concerning measurement, entanglement and even superposition are the foundations of bizarre behaviours in quantum programs. In this paper, we analyse and extend the theoretical theorems on termination analysis proposed by Ying et al. into computational theorems and algorithms. The new algorithm without the Jordan decomposition process has a significant acceleration with polynomial complexity both on terminating and almost-surely terminating programs. Moreover, the least upper bound of termination programs steps is studied and utilized to output the substituted matrix representation of quantum programs. We also implement four groups of experiments to illustrate the advantages of the new algorithm in case of processing a simplified quantum walk example comparing with the original counterpart.

关键词： termination analysis quantum while-language quantum programming quantum experiment terminating steps

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