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A survey on cross-user federated recommendation

science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Separate Source Channel Coding Is Still What You Need:An LLM-Based Rethinking

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ZTE Communications 2025年第1期23卷 30-44页

作者： REN Tianqi LI Rongpeng ZHAO Mingmin CHEN Xianfu LIU Guangyi YANG Yang ZHAO Zhifeng ZHANG Honggang College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Shenzhen CyberAray Network Technology Co. Ltd.Shenzhen 518000China China Mobile Research Institute Beijing 100053China The Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511453China Zhejiang Lab Hangzhou 311121China Faculty of Data Science City University of MacaoMacao 999078China

Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ***,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for *** demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over *** proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point *** comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication *** other words,separate source channel coding is still what we need.

关键词： separate source channel coding(SSCC) joint source channel coding(JSCC) end-to-end communication system Large Language Model(LLM) lossless text compression Error Correction Code Transformer(ECCT)

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Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking

arXiv

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arXiv 2025年

作者： Ren, Tianqi Li, Rongpeng Zhao, Ming-Min Chen, Xianfu Liu, Guangyi Yang, Yang Zhao, Zhifeng Zhang, Honggang College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Shenzhen CyberAray Network Technology Co. Ltd. Shenzhen518000 China China Mobile Research Institute Beijing100053 China The Internet of Things Thrust The Hong Kong University of Science and Technology Guangzhou511453 China Zhejiang Lab Hangzhou311121 China Faculty of Data Science The City University of Macau China

Along with the proliferating research interest in Semantic Communication (SemCom), Joint Source Channel Coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. Nevertheless, this paper challenges the conventional JSCC paradigm, and advocates for adoption of Separate Source Channel Coding (SSCC) to enjoy the underlying more degree of freedom for optimization. We demonstrate that SSCC, after leveraging the strengths of Large Language Model (LLM) for source coding and Error Correction Code Transformer (ECCT) complemented for channel decoding, offers superior performance over JSCC. Our proposed framework also effectively highlights the compatibility challenges between SemCom approaches and digital communication systems, particularly concerning the resource costs associated with the transmission of high precision floating point numbers. Through comprehensive evaluations, we establish that empowered by LLM-based compression and ECCT-enhanced error correction, SSCC remains a viable and effective solution for modern communication systems. In other words, separate source and channel coding is still what we need!. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Be Water, My Antennas: Riding on Radio Wave Fluctuation in Nature for Spatial Multiplexing using Programmable Meta-Fluid Antenna

arXiv

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arXiv 2025年

作者： Liu, Baiyang Tong, Kin-Fai Wong, Kai-Kit Chae, Chan-Byoung Wong, Hang College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering City University of Hong Kong Hong Kong School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electronic and Electrical Engineering University College London LondonWC1E7JE United Kingdom Yonsei Frontier Lab Yonsei University Korea Republic of

Interference and scattering, often deemed undesirable, are inevitable in wireless communications, especially when the current mobile networks and upcoming sixth generation (6G) have turned into ultra-dense networks. Current approaches relying on multiple-input multiple-output (MIMO) combined with artificial-intelligence-aided (AI) signal processing have drawbacks of being power-hungry and requiring wide bandwidth that raise scalability concerns. In this article, we take a radical approach and utilize the channel fading phenomenon to our advantage. Specifically, we propose a novel meta-fluid antenna architecture, referred to as the ‘fluid’ antenna system (FAS), that can freely surf on radio wave fluctuations, like ‘fluid’ figuratively speaking, with fine resolution in space to opportunistically avoid interference, eliminating the need for expensive signal processing. Our experimental results demonstrate that under rich scattering conditions, the proposed meta-fluidic architecture is able to exploit the natural ups and downs of radio waves in space for spatial multiplexing. These breakthrough results show that scattering can be desirable not harmful and interference can be dodged not suppressed, fundamentally changing our perception of fading and our understanding on how interference should be managed in wireless communications networks. © 2025, CC0.

关键词： Fading (radio)

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Weakly-Supervised Depression Detection in Speech Through Self-Learning Based label Correction

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 748-758页

作者： Yanfei Sun Yuanyuan Zhou Xinzhou Xu Jin Qi Feiyi Xu Zhao Ren Björn W. Schuller School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Wuxi University Wuxi China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria Key Laboratory of Modern Acoustics MOE Nanjing University Nanjing China Cognitive Systems Lab University of Bremen Bremen Germany Chair of Health Informatics Technische Universität München (TUM) München Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany GLAM – the Group on Language Audio & Music Imperial College London London U.K.

Automated Depression Detection (ADD) in speech aims to automatically estimate one's depressive attributes through artificial intelligence tools towards spoken signals. Nevertheless, existing speech-based ADD works fail to sufficiently consider weakly-supervised cases with inaccurate labels, which may typically appear in intelligent mental health. In this regard, we propose the Self-Learning-based label Correction (SLLC) approach for weakly-supervised depression detection in speech. The proposed approach employs a self-learning manner connecting a label correction module and a depression detection module. Within the approach, the label correction module fuses likelihood-ratio-based and prototype-based label correction strategies in order to effectively correct the inaccurate labels, while the depression detection module aims at detecting depressed samples through a 1D convolutional recurrent neural network with multiple types of losses. The experimental results on two depression detection corpora show that our proposed SLLC approach performs better compared with existing state-of-the-art speech-based depression detection approaches, in the case of weak supervision with inaccurate labels for depression detection in speech.

关键词： Depression Feature extraction Training Speech processing Prototypes Noise measurement Telecommunications Sun internet of Things Weak supervision

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An Energy-Efficient Framework in Radio Multi-Access Edge Cloud by Automatically Generating Network Slice

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IEEE internet of Things Magazine 2022年第3期5卷 128-133页

作者： Zou, Sai Yu, Haisheng Wang, Wenyong Ni, Wei Guizhou University China University of Electronic Science and Technology China Prospective Technology Lab of the China Future Internet Engineering Center China Macau Univertsity of Science and Technology China Data61 Business Unit CSIRO Australia

Traditional radio multi-access edge computing (RMEC) mainly serves information transmission. However, networking devices tend to be powered by batteries, and the life time of the network is constrained by battery capacity. How to power networking devices in RMEC is a key challenge. Wireless energy transfer (WET), a technology that uses radio frequency signals to charge devices wirelessly, offers the potential to eliminate this performance bottleneck. In this article, a framework for automatically generating network slice (AGNS) is proposed, where 'WET' is set as a special application to realize synchronous transmission of energy and information by network slices. First, we discuss the development trends of RMEC. Next, we embark on customized services and resource efficiency maximization to reduce device's energy consumption, and analyze technical challenges. AGNS provides a logical mapping method to bridge the qualitative demand of new applications and the quantitative supply of slices, and provides a slice orchestration strategy to automatically combine virtualized network functions and optimize virtualized network resources for virtual private network (VPN). AGNS also provides a physical mapping approach to establish the resource corresponding relation between the network slices and physical devices in dynamic environments. Simulation results show that AGNS is superior to existing methods which adopt the 'best effort' mode with a 'one size fits all' rule, in terms of acceptance rate, resource efficiency and energy consumption. The AGNS framework is measured by the data of 10T of China Telecom Sichuan Branch. The results are consistent with our theoretical analysis. © 2018 IEEE.

关键词： Energy utilization

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Trusted Clustering Based Federated Learning in Edge Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Yi-Jing Zhang, Long Li, Xiaoqian Du, Hongyang Feng, Gang Qin, Shuang Wang, Jiacheng University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China Tsinghua University Sichuan Energy Internet Research Institute Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore

Federated learning (FL) is integral to advancing edge intelligence by enabling collaborative machine learning. In FL-empowered edge networks, computing nodes first train local models and then send them to an or multiple aggregation node(s) for global model collaboration. However, the trustworthiness of both local and global models in conventional FL frameworks is compromised due to inadequate model security and transparency. Distributed ledger technique (DLT) can address this issue by leveraging multi-nodes trust capabilities to support distributed consensus. However, model training and consensus performance of DLT may significantly degrade due to instability and resource constraints of edge networks. Sharding technique provides an effective approach by dividing the ledger into smaller and manageable shards. In this paper, to improve model training and consensus performance, we propose a trusted FL framework by incorporating sharding DLT into FL frameworks. We construct a theoretical model to investigate the relationship between model training performance, consensus efficiency, and capacity of edge nodes regarding storage, computing and communications. Based on the theoretical model, we propose a trusted clustering scheme to aggregate local models. Numerical results show that our proposed scheme significantly improves network throughput for transmitting models while guaranteeing model learning performance in comparison with some classical baselines. © 2002-2012 IEEE.

关键词： Distributed ledger

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Lifecycle Management of Trustworthy AI Models in 6G Networks: The REASON Approach

arXiv

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arXiv 2025年

作者： Parra-Ullauri, Juan Zhou, Xueqing Moazzeni, Shadi Hussain, Rasheed Vasilakos, Xenofon Wu, Yulei Baby, Renjith Mahmud, M.M. Hassan Incorvaia, Gabriele Hond, Darryl Asgari, Hamid Tassi, Andrea Warren, Daniel Simeonidou, Dimitra High-Performance Networks Group Smart Internet Lab University of Bristol BS8 1QU United Kingdom Digital Catapult AI and Data Science Technology LondonNW1 2RA United Kingdom Thales UK Research Technology and Solution Innovation Reading United Kingdom United Kingdom

Artificial Intelligence (AI) is expected to play a key role in 6G networks including optimising system management, operation, and evolution. This requires systematic lifecycle management of AI models, ensuring their impact on services and stakeholders is continuously monitored. While current 6G initiatives introduce AI, they often fall short in addressing end-to-end intelligence and crucial aspects like trust, transparency, privacy, and verifiability. Trustworthy AI is vital, especially for critical infrastructures like 6G. This paper introduces the REASON approach for holistically addressing AI’s native integration and trustworthiness in future 6G networks. The approach comprises AI Orchestration (AIO) for model lifecycle management, Cognition (COG) for performance evaluation and explanation, and AI Monitoring (AIM) for tracking and feedback. Digital Twin (DT) technology is leveraged to facilitate real-time monitoring and scenario testing, which are essential for AIO, COG, and AIM. We demonstrate this approach through an AI-enabled xAPP use case, leveraging a DT platform to validate, explain, and deploy trustworthy AI models. Copyright © 2025, The Authors. All rights reserved.

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EPS inhibitor treatment of Salmonella impacts evolution without selecting for resistance to biofilm inhibition

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npj Biofilms and Microbiomes 2025年第1期11卷 1-15页

作者： Joos, Mathieu Van Ginneken, Sybren Villanueva, Xabier Dijkmans, Marie Coppola, Guglielmo A. Pérez-Romero, Camilo Andres Vackier, Thijs Van der Eycken, Erik Marchal, Kathleen Lories, Bram Steenackers, Hans P. KU Leuven - MiCA Lab Centre of Microbial and Plant Genetics Leuven Belgium Department of Chemistry KU Leuven - Laboratory for Organic & Microwave-Assisted Chemistry (LOMAC) Leuven Belgium Department of Plant Biotechnology and Bioinformatics UGent – Internet Technology and Data Science Lab (IDLab) Gent Belgium People’s Friendship University of Russia (RUDN University) Moscow Russian Federation

Virulence factors of pathogens, such as toxin production and biofilm formation, often exhibit a public character, providing benefits to nearby non-producers. Consequently, anti-virulence drugs targeting these public traits may not select for resistance, as resistant mutants that resume production of the virulence factor share the benefits of their resistance with surrounding sensitive cells. In agreement with this, we show that even after long-term treatment with a 2-amino-imidazole (2-AI) biofilm inhibitor, Salmonella populations remained as susceptible to biofilm inhibition as the ancestral populations. Nonetheless, further genotypic and phenotypic analysis revealed that the Salmonella populations did adapt to the treatment and accumulated mutations in efflux pump regulators and alternative sigma factors. These mutations resulted in a reduced biofilm-forming capacity and increased efflux activity. Their selection was due to a growth delaying side effect of the biofilm inhibitor. Enhanced efflux activity helped overcome this growth delay, providing a fitness advantage over the ancestor. Finally, we demonstrate that chemical modification of the inhibitor enhances its specificity by partially alleviating the unintended growth delay while retaining the anti-biofilm activity, which in turn eliminated the selection pressure for increased efflux. Overall, our findings highlight that while unintended side effects can complicate anti-virulence strategies, adaptation to these effects does not necessarily restore the inhibited virulence trait. Moreover, chemical modification can mitigate these unintended side effects and enhance drug specificity. © The Author(s) 2025.

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