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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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A review on machine learning techniques for network security

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Journal of Cyber Security Technology 2025年

作者： Nazir, Rashid Laghari, Asif Ali Dahri, Fida Hussain Shoulin, Yin Alhakeem, Zaineb M. Hakim, Heba Mughal, Zeeshan Ahmed Department of Computer Science Sindh Madressatul Islam University Karachi Pakistan Software college Shenyang Normal University Shenyang China School of Computer Science and Engineering Southeast University Nanjing China Chemical Engineering and Oil Refining Department Oil and Gas Engineering College Basrah University for Oil and Gas Basrah Iraq Computer Engineering Department Engineering College University of Basrah Basrah Iraq

Network security encompasses the strategies and techniques to protect networks from unauthorized access and potential threats. Network security is essential to protect layers of networks and data transferring on them. Data integrity and the underlying network infrastructure are necessary in today’s digital landscape. This paper explores the role of machine learning (ML) in enhancing network security by developing intelligent tools and methodologies. We critically analyze various ML techniques utilized in this field, their practical applications, and the advancements achieved in fortifying networks against cyber threats. We address ML’s challenges and limitations in network security, concluding with a discussion of open research issues for future investigation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Network intrusion

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FedPG: a privacy-friendly and universal method for solving non-IID data in federated learning

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Pattern Analysis and Applications 2025年第2期28卷 1-14页

作者： Xue, Baolu Zhang, Jiale Chen, Bing Meng, Weizhi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China College of Information Engineering Yangzhou University Yangzhou China School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China School of Computing and Communications Lancaster University Lancaster United Kingdom

Federated learning (FL) is a privacy-preserving distributed learning framework which could harness the potential of decentralized multimedia data. However, a significant hurdle lies in the non-uniform distribution of data among clients, leading to slow convergence and subpar accuracy in the global model. Although several approaches have been proposed to address this challenge, two key limitations remain. First, these methods frequently require access to information about local data or even the raw data, which raises significant privacy concerns for clients. Second, these methods struggle to perform well in a common non-IID scenario: class missingness, and they often fail to fully resolve the issue of client drift. In response, in this paper, we propose a privacy-friendly and universal method FedPG to solve non-IID data in FL. The core idea behind FedPG is to leverage homogeneous virtual data to alleviate both data heterogeneity and client drift. Specifically, FedPG introduces a novel image generation method based on prototype loss, which does not require any additional privacy-sensitive information. This approach generates synthetic datasets aligned with the global distribution to effectively assist local training. Besides, we also design a local training method that is suitable for scenarios involving class missingness, enabling both feature adaptation and classifier de-biasing. The comprehensive experiments demonstrate the efficacy of our FedPG framework. In the majority of cases, FedPG not only achieves superior accuracy but also exhibits accelerated convergence rates compared to alternative approaches. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Data heterogeneity Domain adaptation Federated learning Prototype

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MAP-SIM: A DNN-Specific Mapping Optimization Framework for Shared-Memory CPU-Systolic Array Architectures

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Yuhang Wen, Mei Shen, Junzhong Chen, Zhaoyun Shi, Yang Wang, Tianyu Shao, Zili National University of Defense Technology College of Computer Science and Technology Hunan Changsha410073 China Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

As performance demands continue to rise, Shared-Memory Heterogeneous Systems (SMHSs) have been widely adopted for their ability to enable efficient communication and data sharing between different heterogeneous cores. However, existing SMHS face challenges in uneven workload distribution among heterogeneous cores and suboptimal mapping schemes, preventing them from fully leveraging their architectural advantages. To address these issues, this paper proposes a mapping-aware framework for modeling SMHSs called MAP-SIM. By performing performance modeling for CPUs and Systolic Arrays (SAs), and considering rational schemes for the partition and mapping of computational tasks, MAP-SIM aims to evaluate and optimize the computational performance of heterogeneous multicore architectures. The experimental results show that compared to previous work, MAP-SIM can increase simulation speed by 14 to 67 times and can also enhance the computational performance of SMHS by 1.4 to 4.4 times. © 1982-2012 IEEE.

关键词： Photomapping

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EF-IMR: Embedded Flash with Interlaced Magnetic Recording Technology

EF-IMR: Embedded Flash with Interlaced Magnetic Recording Te...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Chenlin Ma Xiaochuan Zheng Kaoyi Sun Tianyu Wang Yi Wang College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Radio Frequency Heterogeneous Integration

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Interlaced Magnetic Recording (IMR), a technology that improves storage density through track overlap, introduces significant latency due to Read-Modify-Write (RMW) operations. Writing to overlapped tracks affects underlying tracks, requiring additional I/O operations to read, back up, and rewrite them, resulting in significant head movement latency. We propose EF-IMR, a new architecture that ensures crash consistency in IMR while minimizing RMW latency and head movement. EF-IMR reduces head movement during RMW operations and decreases redundant RMW operations. Evaluations under real-world, intensive I/O workloads show that EF-IMR reduces RMW latency by 20.11 % and head movement latency by 89.37% compared to existing methods.

关键词： Tracking Europe Writing Magnetic heads computer crashes Delays Magnetic recording

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Efficient High-Precision Classification Algorithm for Radar Deceptive Jamming via Array Detection

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Progress in Electromagnetics Research C 2025年 155卷 29-35页

作者： Liu, Wenjie Qin, Wenfang Shen, Mingwei Han, Guodong College of Information Science and Engineering Hohai University Jiangsu Changzhou213200 China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing211106 China The 54th Research Institute of CETC Hebei Shijiazhuang050081 China

In complex electromagnetic scenarios where multiple deceptive jamming signals are simultaneously aliased in the time frequency domain, conventional single-channel electronic detection systems struggle to effectively separate and classify overlapping jamming sources. To address this limitation, this paper investigates an array detection-based classification scheme for multi-source jamming. First, due to the spatial degrees of freedom offered by array systems, the Direction-of-Arrival (DOA) of each jamming source is precisely estimated using the MUSIC algorithm. Then, the adaptive digital beamforming (ADBF) filters are designed based on the estimated DOAparameters, enabling spatial-domain extraction of individual jamming signals. Finally, the DOA information is integrated into the Pulse Description Word (PDW) of each separated jamming measurement, which can facilitate adaptive K-Means clustering with enhanced class separability. Simulation results demonstrate that the proposed method achieves a 22.4% improvement in classification accuracy compared to existing single-channel detection approaches, while maintaining computational efficiency. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Frequency response

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Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images

arXiv

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arXiv 2025年

作者： Fang, Sheng Li, Kaiyu Li, Zhe Zhao, Jianli Zhang, Xingli College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China

Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical remote sensing

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

引用

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

作者： Zhang, Haodi Wang, Yichi Jian, Yifan Jiang, Jiahui Bai, Zhaohai Ma, Lin College of Computer Science and Software Engineering Shenzhen University China Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences China State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University China

ISBN: (纸本)9798350368741

Climate downscaling is crucial for detailed small-scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State-Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-ofthe-art performance and provides strong support for climate downscaling and the planning of climate-related strategies. © 2025 IEEE.

关键词： Climate downscaling Deep Learning Multimodal fusion Operator learning Time series forecasting

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Semantic consistency assisted change detection in remote sensing images

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National Remote Sensing Bulletin 2025年第4期29卷 1031-1045页

作者： Wu, Hucheng Wang, Renfang Qiu, Hong Wang, Feng Gao, Guang Wu, Dun College of Computer Science and Technology Ocean University of China Qingdao266100 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China Co. Ltd. Ningbo315042 China

Semantic change detection of remote sensing images plays an important role in ecological environment, land use, and land cover monitoring. In recent years, deep learning-based change detection methods are the hotspot of remote sensing intelligent interpretation concern;however, the existing three-branch semantic change detection methods lack consistency of change branch and semantic branches, which leads to the self-contradiction of the bitemporal semantic change detection. To address this problem, this study proposes a remote sensing image semantic change detection algorithm based on Siamese CNN and Transformer. In the encoding stage, the Siamese ResNet34 network is first designed to extract the multiscale features of the image, and the difference enhancement module is embedded to increase the attention of the change information;then, the semantic tokenizer is used to map the feature map into compact semantic tokens, and the Transformer encoder is used to combine the bitemporal semantics and the change information to model the"semantic change"consistency. In the decoding stage, different fine-grained semantic information is fused by the Transformer decoder using a hopping connection to generate a refined semantic feature map. Finally, the result of bitemporal semantic change is obtained after upsampling recovery and mask multiplication. Experimental results on the remote sensing semantic change detection public dataset SECOND and Landsat-SCD show that the proposed algorithm can effectively focus on the change region, maintain the consistency between the change results and the semantic results, and achieve excellent evaluation indexes and visual effects. We can draw the following conclusions: (1) The proposed difference enhancement module can enhance the difference characteristics of bitemporal remote sensing images and improve the network’s focus on change information. (2) The proposed bitemporal Transformer module maps the difference information and bitemporal sem

关键词： Change detection

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Decentralized Self-Auditing Multiple Cloud Storage in Compressed Provable Data Possession

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Yang, Yang Chen, Yanjiao Xiong, Ping Chen, Fei Chen, Jing Zhongnan University of Economics and Law School of Information Engineering Wuhan China Zhejiang University College of Electrical Engineering Hangzhou China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Wuhan University School of Cyber Science and Engineering Wuhan China

As cloud storage becomes popular, more and more users tend to outsource their data to powerful cloud severs. To prevent a single point of failure, users prefer to store data on multiple cloud servers from different cloud service providers. However, after outsourcing data to cloud servers, users lose the control of their data, which may incur many serious security issues, such as abnormal data tampering and deleting. It is necessary for users to audit multiple cloud storage aperiodically. In this paper, we aim to design a decentralized self-auditing solution for multiple cloud storage, in which cloud servers can audit the integrity of each other, and thus no third-party entity is required. Firstly, based on basic algebra, our protocol realizes decentralized self-auditing multiple cloud storage that only involves encrypted user data. Secondly, we design a proof exchanging mechanism, making any number of cloud servers form the same final integrity proof with a linear number of interactions. Thirdly, our solution can achieve cloud dynamics, which can freely enable and disable a cloud server to provide storage service. At last, security proof and performance evaluation show that the proposed protocol has provable security and high efficiency. © 2004-2012 IEEE.

关键词： Cloud security

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