IEEE journal on selected areas in communications : a publication of the IEEE Communications Society最新文献

IEEE Journal on Selected Areas in Communications Publication Information IEEE通讯出版信息选定领域期刊

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-18 DOI: 10.1109/JSAC.2025.3576465

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Guest Editorial: Rethinking the Information Identification, Representation, and Transmission Pipeline: New Approaches to Data Compression and Communication 客座评论：信息识别、表示和传输管道的再思考：数据压缩和通信的新方法

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-18 DOI: 10.1109/JSAC.2025.3557932

Jun Chen;Alexandros G. Dimakis;Yong Fang;Ashish Khisti;Ayfer Özgür;Nir Shlezinger

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IEEE Communications Society Information IEEE通信学会信息

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-06-18 DOI: 10.1109/JSAC.2025.3576467

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Knowledge-Aware Privacy-Preserving Model Customization in Zero-Trust Federated Learning Model Marketplaces 零信任联邦学习模型市场中知识感知的隐私保护模型定制

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-28 DOI: 10.1109/JSAC.2025.3560010

Yanghe Pan;Zhou Su;Yuntao Wang;Han Liu;Ruidong Li;Abderrahim Benslimane

{"title":"Knowledge-Aware Privacy-Preserving Model Customization in Zero-Trust Federated Learning Model Marketplaces","authors":"Yanghe Pan;Zhou Su;Yuntao Wang;Han Liu;Ruidong Li;Abderrahim Benslimane","doi":"10.1109/JSAC.2025.3560010","DOIUrl":"10.1109/JSAC.2025.3560010","url":null,"abstract":"Federated learning (FL) model marketplaces require qualified workers to collaboratively train customized models. However, recruiting optimal workers on a limited budget in non-independent and identically distributed (non-IID) data settings remains a fundamental issue. Moreover, inadequate quality verification exposes the marketplace to spoofing and poisoning attacks, while verifying data and model quality without accessing local storage remains a significant dilemma. To bridge the research gap, this paper proposes a knowledge-aware model customization scheme in FL model marketplaces, to facilitate zero-trust worker recruitment and verification while ensuring privacy preservation. Specifically, (i) we design a knowledge-aware quality evaluation mechanism by leveraging the knowledge of workers, i.e., soft-label predictions of their local models on a privacy-free reference dataset (provided by the customer), to assess their data quality in a privacy-preserving manner. (ii) We formulate the optimal worker recruitment problem under budget constraints as an NP-hard integer programming problem and design a dynamic programming-based optimal worker recruitment algorithm with budget feasibility and computational efficiency. (iii) We devise a two-stage zero-trust quality verification mechanism by utilizing zero-knowledge proof (ZKP) to exclude distrustful workers, thereby preventing spoofing and poisoning attacks. Extensive experimental results demonstrate that the proposed scheme enhances model customization performance by up to 34.3% on label-skewed non-IID data and 36.2% on feature-skewed non-IID data compared with existing representatives.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 6","pages":"1923-1937"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Object-Attribute-Relation Representation-Based Video Semantic Communication 基于对象-属性-关系表示的视频语义通信

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-22 DOI: 10.1109/JSAC.2025.3559115

Qiyuan Du;Yiping Duan;Qianqian Yang;Xiaoming Tao;Mérouane Debbah

{"title":"Object-Attribute-Relation Representation-Based Video Semantic Communication","authors":"Qiyuan Du;Yiping Duan;Qianqian Yang;Xiaoming Tao;Mérouane Debbah","doi":"10.1109/JSAC.2025.3559115","DOIUrl":"10.1109/JSAC.2025.3559115","url":null,"abstract":"With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding (JSCC) that depends on end-to-end training. These methods often lack an interpretable semantic representation and struggle with adaptability to various downstream tasks. In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission. We utilize OAR sequences for both low bit-rate representation and generative video reconstruction. Additionally, we incorporate OAR into the image JSCC model to prioritize communication resources for areas more critical to downstream tasks. Our experiments on traffic surveillance video datasets assess the effectiveness of our approach in terms of video transmission performance. The empirical findings demonstrate that our OAR-based video coding method not only outperforms H.265 coding at lower bit-rates but also synergizes with JSCC to deliver robust and efficient video transmission.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 7","pages":"2446-2461"},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Analysis of Channel Uncertainty in Trusted Wireless Services via Repeated Interactions 基于重复交互的可信无线业务信道不确定性分析

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-21 DOI: 10.1109/JSAC.2025.3560005

Bingwen Chen;Xintong Ling;Weihang Cao;Jiaheng Wang;Zhi Ding

{"title":"Analysis of Channel Uncertainty in Trusted Wireless Services via Repeated Interactions","authors":"Bingwen Chen;Xintong Ling;Weihang Cao;Jiaheng Wang;Zhi Ding","doi":"10.1109/JSAC.2025.3560005","DOIUrl":"10.1109/JSAC.2025.3560005","url":null,"abstract":"The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications; however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 6","pages":"2248-2265"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Layer Management Framework for Enhancing XR-Based System Security in Zero-Trust Wireless Communications 在零信任无线通信中增强基于 XR 的系统安全的跨层管理框架

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-21 DOI: 10.1109/JSAC.2025.3560004

Esraa M. Ghourab;Mohamed Azab;Denis Gračanin;Mahmoud Al-Qutayri;Sami Muhaidat

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Protection Against Poisoning Attacks on Federated Learning-Based Spectrum Sensing $$ $ lg $$ $ }} ?> 基于联邦学习的频谱感知防中毒攻击

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-16 DOI: 10.1109/JSAC.2025.3560285

Małgorzata Wasilewska;Hanna Bogucka

{"title":"Protection Against Poisoning Attacks on Federated Learning-Based Spectrum Sensing $$ $ lg $$ $ }} ?>","authors":"Małgorzata Wasilewska;Hanna Bogucka","doi":"10.1109/JSAC.2025.3560285","DOIUrl":"10.1109/JSAC.2025.3560285","url":null,"abstract":"Federated-Learning (FL) based Spectrum Sensing (SS) method is considered for the application in future cognitive radio communication systems due to its supreme performance in changing radio environments as compared to classic cooperative or non-cooperative SS. It also avoids transferring large training datasets with high-resolution localization data. The FL algorithm is the subject of poisoning attacks that can be random or coordinated. In this paper, we first evaluate the impact of such attacks on the FL-based SS performance. Next, we propose a zero-trust method based on continuous monitoring and classification of the sensors’ models to detect attacked models. These models are then eliminated from the global model construction in FL. Our method is semi-blind, i.e., it does not require an apriori knowledge of who are the genuine actors participating in FL. Simulation results of the system under various attacks (random or coordinated, moderate or very aggressive, deliberately increasing or decreasing the spectrum occupancy) show that our method decreases the SS probability of false alarms by 89 % and increases the SS probability of detection by 16 % in case of the most severe targeted attacks in the most critical SNR ranges.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 6","pages":"2042-2055"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SecureFedPROM: A Zero-Trust Federated Learning Approach With Multi-Criteria Client Selection SecureFedPROM：一种多标准客户端选择的零信任联邦学习方法

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-16 DOI: 10.1109/JSAC.2025.3560008

Mehreen Tahir;Tanjila Mawla;Feras Awaysheh;Sadi Alawadi;Maanak Gupta;Muhammad Intizar Ali

{"title":"SecureFedPROM: A Zero-Trust Federated Learning Approach With Multi-Criteria Client Selection","authors":"Mehreen Tahir;Tanjila Mawla;Feras Awaysheh;Sadi Alawadi;Maanak Gupta;Muhammad Intizar Ali","doi":"10.1109/JSAC.2025.3560008","DOIUrl":"10.1109/JSAC.2025.3560008","url":null,"abstract":"Federated Learning (FL) enables decentralized learning while preserving data privacy. However, ensuring security and optimizing resource utilization in FL remains challenging, particularly in untrusted environments. To address this, we propose SecureFedPROM, a novel zero-trust FL framework that integrates Attribute-Based Access Control (ABAC) for secure client authorization and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) for dynamic, multi-criteria client selection. Unlike traditional FL client selection methods that prioritize security or efficiency, SecureFedPROM optimizes trustworthiness, computational efficiency, and performance, ensuring robust participation in each training round. We evaluate SecureFedPROM across multiple real-world datasets, demonstrating its superiority over state-of-the-art client selection protocols. Our results show that SecureFedPROM achieves a 7.19% improvement in model accuracy, accelerates convergence, and reduces the number of training rounds. Additionally, it minimizes wall-clock time and computational overhead, making it highly scalable for edge AI environments. These findings highlight the importance of integrating zero-trust security principles with multi-criteria decision-making to enhance security and efficiency in FL.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 6","pages":"2025-2041"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10966024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Byzantine-Resilient Over-the-Air Federated Learning Under Zero-Trust Architecture 零信任架构下的拜占庭弹性空中联邦学习

IEEE journal on selected areas in communications : a publication of the IEEE Communications Society Pub Date : 2025-04-15 DOI: 10.1109/JSAC.2025.3560046

Jiacheng Yao;Wei Shi;Wei Xu;Zhaohui Yang;A. Lee Swindlehurst;Dusit Niyato

{"title":"Byzantine-Resilient Over-the-Air Federated Learning Under Zero-Trust Architecture","authors":"Jiacheng Yao;Wei Shi;Wei Xu;Zhaohui Yang;A. Lee Swindlehurst;Dusit Niyato","doi":"10.1109/JSAC.2025.3560046","DOIUrl":"10.1109/JSAC.2025.3560046","url":null,"abstract":"Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 6","pages":"1954-1969"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0