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A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments

IEEE Communications Surveys and Tutorials 2025年

作者： Jia, Ninghui Qu, Zhihao Ye, Baoliu Wang, Yanyan Hu, Shihong Guo, Song Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Nanjing211100 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Kowloon Hong Kong

In traditional centralized machine learning, transmitting raw data to a cloud center incurs high communication costs and raises privacy concerns. This is particularly challenging in mobile edge environments, where devices are dynamic and resource-constrained. Federated Learning (FL) addresses these issues by allowing devices to train models locally and upload parameters to a central server without sharing local data. However, limited wireless channel resources and dynamic transmission performance make communication overhead a major bottleneck of FL in mobile edge environments. Concerning this challenge, this survey provides a comprehensive summary of methods to improve communication efficiency in FL, focusing on: 1) minimizing communication complexity to reduce total transmission volume, 2) scheduling resources appropriately to improve training efficiency, 3) utilizing over-the-air computation (OTA) to integrate computation into communication for accommodating the computation/communication characteristics of FL in mobile edge environments. Thus, this work analyzes research from the perspective of convergence and data heterogeneity to reduce communication rounds by optimizing algorithm performance. We hope that this survey could offer insights into communication-efficient FL for future research. © 2025 IEEE.

关键词： Federated learning

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Spatiotemporal changes and interconnections between meteorological and hydrological droughts in China over past 34 years

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water Science and Engineering 2025年

作者： Zhang, Ke Li, Zhi-lin Shi, Wu-zhi Tao, Ran Yang, Xu Huang, Yi-ming National Key Laboratory of Water Disaster Prevention Hohai University Nanjing210098 China College of Hydrology and Water Resources Hohai University Nanjing210098 China Yangtze Institute for Conservation and Development Nanjing210098 China China Meteorological Administration Hydro-Meteorology Key Laboratory Hohai University Nanjing210098 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210098 China

Understanding the evolution and lag effects of droughts is critical to effective drought warning and water resources management. However, due to limited hydrological data, few studies have examined hydrological droughts and their lag time from meteorological droughts at a daily scale. In this study, precipitation data were collected to calculate the standardized precipitation index (SPI), and runoff data simulated by the variable infiltration capacity (VIC) model were utilized to compute the standardized runoff index (SRI). The three-threshold run theory was used to identify drought characteristics in China. These drought characteristics were utilized to investigate spatiotemporal variations, seasonal trends, and temporal changes in areas affected by meteorological and hydrological droughts. Additionally, the interconnections and lag effects between meteorological and hydrological droughts were explored. The results indicated that (1) drought occurred during approximately 28% of the past 34 years in China;(2) drought conditions tended to worsen in autumn and weaken in winter;(3) drought-affected areas shifted from northwest to northeast and finally to southern China;and (4) the correlation between meteorological and hydrological droughts was lower in the northwest and higher in the southeast, with all correlation coefficients exceeding 0.7. The lag times between meteorological and hydrological droughts were longest (5 d) in the Yangtze River, Yellow River, and Hai River basins, and shortest (0 d) in the Tarim River Basin. This study provides a scientific basis for effective early warning of droughts. © 2025

关键词： Drought

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ApriDS: An evidence linkage network construction model for judgment documents

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-13页

作者： Zhou, Yulin Qin, Yongbin Lin, Chuan Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 China College of Computer Science & Technology Guizhou University Guiyang 550025 China

Evidence serves as the basis for determining facts in the judicial trial process, and exploring the correlation between evidence has become an essential task. However, there is uncertainty and unreliability of evidence. In this paper, we propose an ApriDS model that can mine correlations between evidence and construct evidence linkage networks. Specifically, we use the association rule algorithm to mining for the correlations between evidence in order to initially construct an evidence linkage network. Moreover, we also combine the imprecise reasoning theory to alleviate unreliability among evidence. Finally, combining the above effectively mitigates uncertainty and unreliability in the evidence linkage network. We conduct extensive experiments and present analyses on five case datasets, including marriages, thefts, homicides, contract disputes and unlawful detention. Compared to the baseline model, our method’s accuracy improves by 12.75%, 14.16%, 14.80%, 21.64% and 26.15% on the five datasets, respectively. © The Author(s) 2025.

关键词： Apriori algorithm D-S evidence theory Evidence linkage network Judgment document

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Multi-Hop Attention Diffusion Graph Neural Networks For Multimodal Fake News Detection

Multi-Hop Attention Diffusion Graph Neural Networks For Mult...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Zhongqiang Lu, Dongli Sha, Ying Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China College of Informatics Huazhong Agricultural University Wuhan China

ISBN: (纸本)9798350368741

Fake news often spreads rapidly on social media in various forms, exposing users to a large amount of misinformation and disinformation. Recently, promising results have been achieved in fake news detection. However, most of the approaches only consider one-hop neighborhood information in cross-modal mapping relationships or focus mainly on pairwise interactions of multimodal fake news. However, the fine-grained feature mapping of multimodal fake news may go beyond binary relations. To address these issues, we propose a Multi-Hop Attention Diffusion Graph Neural Networks in this work to capture and complement the multivariate mapping relationships between fine-grained features, while extracting cross-modal higher-order alignment representations for multimodal fake news detection via cross-modal contrastive learning and modal matching. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach in the multimodal fake news detection task. © 2025 IEEE.

关键词： attention diffusion fake news graph neural networks multivariate mapping relationships

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Priority-Based Blockchain Packing for Dependent Industrial IoT Transactions

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1618-1628页

作者： Lin, Chaofeng Tang, Jinchuan Dang, Shuping Chen, Gaojie Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Guangdong Shenzhen518107 China 5GIC and 6GIC The University of Surrey United Kingdom

Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations. © 2004-2012 IEEE.

关键词： Industrial internet of things (IIoT)

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

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Unmanned aerial vehicle remote sensing and machine learning techniques to predict CODMnin Taizhou

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Zhang, Peili Song, Xiaoqing Song, Liangchu Wang, Hao Gu, Binghua Taizhou Pollution Control Technology Center Co. Ltd. Taizhou China Key Laboratory of the Eco-Environmental Big Data of Taizhou Taizhou China

To rapidly and accurately determine the spatial distribution of permanganate index (CODMn) concentrations in surface water, we employed multispectral unmanned aerial vehicle (UAV) remote sensing for water quality inversion monitoring. River extraction was conducted using the normalized difference water index (NDWI) in conjunction with a self-developed parameter a. Fifteen characteristic variables were derived through exponential calculation, and modeling was performed in Jiaobei River using both single-band variables and those with a correlation coefficient exceeding 0.3 and tested in Nanguan River. The model selection included the elastic net regression algorithm, random forest regression algorithm, and gradient boosting regression algorithm. The findings indicated that accurate river extraction is achieved when NDWI exceeds 0.2 and a remains below 0. The gradient boosting regression algorithm proved to be the superior method for constructing an inversion model of CODMn in the Jiaobei River. When applied to the Nanguan River, the model also yielded reliable inversion results. However, in the concentration range of 3 to 5mg/L, the inversion results were less accurate due to an insufficient sample size. A quantitative remote sensing monitoring model of water quality in Taizhou was established using a multispectral UAV, providing technical support for accurate water pollution control. © 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： water pollution control

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AutoMiner: Reinforcement Learning-Based Mining Attack Simulator 24th

AutoMiner: Reinforcement Learning-Based Mining Attack Simula...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Wei Xue, Lide Han, Ziyang Chen, Bingren Zhang, Xishan Zhou, Xuehai University of Science and Technology of China Hefei China ShangHai Municipal Big Data Center Shanghai China Anban Tech Co. Shanghai China State Key Laboratory of Processor Technologies Institute of Computing Technology Chinese Academy of Sciences Beijing China Cambricon Technologies Beijing China

ISBN: (纸本)9789819615247

As blockchain technology rapidly advances, it faces significant security threats from attacks like selfish mining, which exploit consensus algorithm vulnerabilities, undermining system security. Traditional analysis methods, primarily reliant on Markov models, are often utilized to address these complex threats. However, these methods frequently fall short in accurately simulating the multifaceted nature of blockchain attacks, leading to gaps in security measures. To bridge this gap, we introduce AutoMiner, an innovative reinforcement learning-based framework that integrates Miner Monte Carlo Tree Search (MMCTS) with Long Short-Term Memory (LSTM) networks for simulating and detecting potential mining attacks within the Proof of Work (PoW) framework. Our experimental results demonstrate AutoMiner’s capability to outperform traditional selfish and honest mining strategies, achieving up to 20% higher profits under specific scenarios. This highlights AutoMiner’s potential in enhancing blockchain security by providing a more comprehensive and effective approach to analyzing and mitigating mining attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Reinforcement learning Security analysis

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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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