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Optimizing Model-Driven Federated Learning for Rational and Data-Efficient in Social Mobile Network

IEEE Network 2024年

作者： Lu, Jianfeng Zhang, Ying Cao, Shuqin Wang, Wei Tang, Changbing Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China

With the rise of data-intensive services in mobile networks, the demand for intelligent and efficient learning frameworks has grown exponentially. Federated Learning (FL), as an emerging paradigm, enables decentralized edge devices to collaboratively train large models without sharing raw data, ensuring privacy preservation. However, deploying FL in resource-constrained mobile environments faces significant challenges, such as free-riding and malicious behavior. These challenges hinder model performance and raise concerns about data integrity and inter-user cooperation. To address these issues, we propose a layered dynamic framework for social mobile networks, called FedSMN, which aims to enhance the generalization ability of collaboratively trained large models in FL environments. FedSMN leverages Bayesian inference to evaluate user behavior and employs evolutionary game theory to simulate dynamic interactions, thereby optimizing cooperation strategies and reducing communication overhead. Unlike traditional approaches that assume fully rational actors, FedSMN accounts for irrational behavior and fluctuating resources, designing an efficient incentive mechanism to improve data utilization. Extensive experiments on large-scale datasets demonstrate that FedSMN increases model accuracy by 21% and reduces communication overhead by 49%, paving the way for scalable, intelligent mobile networks powered by large models. © 1986-2012 IEEE.

关键词： Collaborative learning

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TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning

arXiv

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arXiv 2024年

作者： Hu, Gangqiang Lu, Jianfeng Han, Jianmin Cao, Shuqin Liu, Jing Fu, Hao School of Computer Science and Technology Zhejiang Normal University China School of Computer Science and Technology Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Universityof Science and Technology China

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semidecentralized FL, clients' communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi- Markov model to estimate clients' communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss. © 2024, CC BY.

关键词： Federated learning

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Robust Beamforming for Downlink Multi-Cell systems: A Bilevel Optimization Perspective

arXiv

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arXiv 2024年

作者： Chen, Xingdi Xiong, Yu Yang, Kai Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming techniques that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender systems

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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Using Experience Classification for Training Non-Markovian Tasks

SSRN

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SSRN 2023年

作者： Miao, Ruixuan Lu, Xu Tian, Cong Yu, Bin Cui, Jin Duan, Zhenhua Institute of Computing Theory and Technology State Key Laboratory of Integrated Services Networks Xidian University China School of Computer Science Xi’an Shiyou University China

Unlike the standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, whose rewards are predicated on state history rather than solely on the current state. Solving a non-Markovian task, frequently applied in practical applications such as autonomous driving, financial trading, and medical diagnosis, can be quite challenging. We propose a novel RL approach to achieve non-Markovian rewards expressed in temporal logic LTLf (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTLf into MDPs (Markov Decision Processes) is introduced to take advantage of advanced RL algorithms. Then, a prioritized experience replay technique based on the automata structure (semantics equivalent to LTLf specification) is utilized to improve the training process. We empirically evaluate several benchmark problems augmented with non-Markovian tasks to demonstrate the feasibility and effectiveness of our approach. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Sustainable and Trusted Vehicular Energy Trading Enabled by Scalable Blockchains

Sustainable and Trusted Vehicular Energy Trading Enabled by ...

引用

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

作者： Qingmei Yang Lijun Sun Xiao Chen Lingling Wang College of Information Science and Technology Qingdao University of Science and Technology Shandong China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Computing and Mathematical Sciences University of Leicester Leicester UK

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The integration of electric vehicles (EVs) into the transportation network has positioned their battery packs not only as power sources for mobility but also as crucial components of energy storage. This dual role enables EVs to trade energy with the grid and among themselves, introducing an innovative approach to energy transactions. Their ability to mitigate peak energy demand and engage in vehicle-to-vehicle (V2V) exchanges represents a significant step toward a more interactive and adaptable energy infrastructure. This paper introduces a blockchain-based V2V energy trading system that utilizes EVs as mobile energy storage units to effectively promote the trustworthiness of energy trading in an environment of mutual distrust through decentralized protocols. The system includes a V2V energy trading protocol that facilitates secure energy transactions, guarantees data integrity, and builds trust among participants. Additionally, it employs a new group fairness-based trade matching algorithm for sustainable energy trading services, encouraging greater participation from EV owners and fostering a more responsive energy market. To support these decentralized and trusted V2V trading services, an efficient and scalable blockchain prototype is proposed. This prototype features a novel sharding consensus scheme, which has been implemented and deployed for real-world experiments and performance evaluation. This system contributes to the green energy economy by enabling a transparent and trust-based approach to energy trading, offering a transformative model for sustainable energy commerce.

关键词： Green energy Sharding Scalability Smart contracts Prototypes Vehicular ad hoc networks Transportation Throughput Security Vehicle dynamics

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Byzantine-Robust Hierarchical Aggregation for Cross-Device Federated Learning in Consumer IoT

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Liu, Jingwei Wu, Yufeng Du, Wei Sun, Rong Xu, Guangxia Liu, Lei Wu, Celimuge Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi’an China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Guangzhou Institute of Technology Xidian University Guangzhou China Meta-Networking Research Center University of Electro-Communications Tokyo Japan

Nowadays, Federated Learning (FL) has emerged as a prominent technique of model training in Consumer Internet of Things (CIoT) without sharing sensitive local data. Targeting privacy leakage of cross-device FL in CIoT, various privacy-preserving FL schemes have been proposed. Regrettably, existing schemes still face three significant challenges: 1) Current privacy-preserving strategies struggle to fully defend against Byzantine attacks in FL without compromising data privacy;2) Most privacy-preserving techniques (e.g. secret sharing) in FL result in substantial computation and communication overhead;3) The non-colluding dual-server setting limits the applicability of FL. To overcome these challenges, we propose a Byzantine-robust hierarchical federated learning scheme, named BHFL. This scheme not only effectively defends against Byzantine attacks while safeguarding user privacy but also avoids the need for a dual-server architecture. Simultaneously, the hierarchical aggregation structure can effectively train non-IID cross-device data while maintaining high communication efficiency. We evaluate BHFL on several benchmark datasets, and the experimental results demonstrate that BHFL achieves high accuracy and Byzantine robustness compared to the popular FedAvg scheme. Therefore, BHFL is well-suited for CIoT scenarios. IEEE

关键词： Differential privacy

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Low-Complexity and Interpretable Machine Learning Method for Multiple Parameters Estimation

Low-Complexity and Interpretable Machine Learning Method for...

引用

OptoElectronics and Communications Conference, OECC

作者： Zhiquan Wan Zhenming Yu Kun Xu Kun Yin Hui Yu Chuliang Hu Zhejiang Lab Research Center of High Efficiency Computing System Hangzhou China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Sciences Institute of Innovative Computing Technology Hangzhou China

We experimentally demonstrate a random forest- based method for simultaneous modulation format identification and OSNR monitoring. Compared with neural network-based method, this method is interpretable and reduces co... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Radio frequency Accuracy Parameter estimation Estimation Forestry Optical noise Optical modulation Monitoring Random forests Signal to noise ratio

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

引用

Congress on Evolutionary Computation

作者： Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time.

关键词： Costs Sociology Evolutionary computation Statistics Optimization Standards

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A Four-Pronged Defense Against Byzantine Attacks in Federated Learning

arXiv

引用

arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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