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Privacy-aware service placement for mobile edge computing via federated learning
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“Further, Qian et al., (2019) devoted to utilizing FL on MEC. They developed a privacy-aware service placement scheme to provide high-quality service by caching desired service on the edge server close to the users.”
From A review of applications in federated learning (2020) · cites this paper as Qian et al., (2019)
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Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565
“The authors in [177] introduce a privacy-aware service”
BackgroundA review of applications in federated learning
Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106854 · Cited by 1,559
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407
Yin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638
“Therefore, mobile edge computing emphasizes the development of network communication techniques and can benefit the development of FL. Recently, several research studies on FL in mobile edge computing have been proposed [49, 105, 116, 140, 189, 190, 201, 220].”
BackgroundResource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
Federated learning review: Fundamentals, enabling technologies, and future applications
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556
A federated learning system with enhanced feature extraction for human activity recognition
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Task offloading strategies for mobile edge computing: A survey
Dong, S., Tang, J., Abbas, K., Hou, R., Kamruzzaman, J., Rutkowski, L., & Buyya, R. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110791 · Cited by 178
Federated Learning for Edge Computing: A Survey
Brecko, A., Kajáti, E., Koziorek, J., & Zolotová, I. (2022) · Applied Sciences · doi:10.3390/app12189124 · Cited by 181
“Qian et al. [109] propose a privacy-aware service placement scheme to address the problem of service placement with privacy-awareness in the edge cloud system using FL.”
MethodShuja, J., Bilal, K., Alasmary, W., Sinky, H., & Alanazi, E. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103005 · Cited by 162
“e 7 illustrates the difference between different learning approaches in edge networks (federated, MEC, cloud) to highlight the benefits of federated learning towards privacypreserving mechanisms [34], [129]. C. Reinforcement Learning Most of ML-based caching techniques in wireless networks apply RL to solve a sub-probl…”
BackgroundFederated Neural Architecture Search for Medical Data Security
Liu, X., Zhao, J., Li, J., Cao, B., & Lv, Z. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3144016 · Cited by 152
Federated learning in smart cities: Privacy and security survey
Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 165
Machine Learning in IoT Security: Current Solutions and Future Challenges
Hussain, F., Hussain, R., Hassan, S. A., & Hossain, E. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986444 · Cited by 129
“In essence, Generative Adversarial Networks (GANs) [194] and distributed learning (Federated Learning) [195]–[197] have recently gained a lot of attention from research community.”
BackgroundHybrid Blockchain-Based Resource Trading System for Federated Learning in Edge Computing
Fan, S., Zhang, H., Zeng, Y., & Cai, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028101 · Cited by 125
Mechanisms for Resource Allocation and Pricing in Mobile Edge Computing Systems
Bahreini, T., Badri, H., & Grosu, D. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3099731 · Cited by 106
“Service placement has been another area of interest for researchers, that is, determining the most efficient server to run a service, where servers could be located in clouds or at the edge of the network [18], [19], [20], [21], [22].”
BackgroundResource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning
Zhu, T., Zhou, W., Ye, D., Cheng, Z., & Li, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086910 · Cited by 109
Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review
Malazi, H. T., Chaudhry, S. R., Kazmi, A., Palade, A., Cabrera, C., White, G., & Clarke, S. (2022) · IEEE Access · doi:10.1109/access.2022.3160738 · Cited by 100
“The first group of attributes are demo-graphics such as age, gender, nationality, occupation, and education [82, 116, 204, 208].”
BackgroundMethodInfluentialSecurity and Privacy Threats to Federated Learning: Issues, Methods, and Challenges
Zhang, J., Zhu, H., Wang, F., Zhao, J., Xu, Q., & Li, H. (2022) · Security and Communication Networks · doi:10.1155/2022/2886795 · Cited by 105
“'e distributed data architecture always keeps training data locally rather than transferring it to the cloud for centralized storage and processing [7], which significantly reduces network bandwidth and energy consumption, especially in tasks involving unstructured data.”
BackgroundA Survey on federated learning
Li, L., Fan, Y., & Lin, K. (2020) · International Conference on Control and Automation · doi:10.1109/icca51439.2020.9264412 · Cited by 104
“Study of FL can be divided into two main areas: the first one is the extension of Google’s work on decentralized model for distributed mobile devices such as prediction on keyboard [49], and service placement for mobile edge computing [50].”
BackgroundEnabling technologies for AI empowered 6G massive radio access networks
Shahjalal, M. A., Kim, W., Khalid, W., Moon, S., Khan, M. A., Liu, S., Lim, S., Kim, E., Yun, D.-W., Lee, J., et al. (2022) · ICT Express · doi:10.1016/j.icte.2022.07.002 · Cited by 92
Federated learning in intrusion detection: advancements, applications, and future directions
Büyüktanır, B., Altınkaya, Ş., Baydoğmus, G. K., & Yıldız, K. (2025) · Cluster Computing · doi:10.1007/s10586-025-05325-w · Cited by 111
“For instance, this method is particularly valuable in industries such as IoT [77] and Blockchain [78, 79], mobile network [80, 81], sales [82], finance [83], transportation [84], and healthcare [85, 86] where data privacy, security, and intellectual property concerns prohibit the central aggregation…”
Federated Learning and Its Role in the Privacy Preservation of IoT Devices
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“Additionally, it is almost impossible to collect the data of all parties without the consent of the users [28,57].”
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A review on federated learning towards image processing
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“Furthermore, Qian et al. [27] focused on the application of FL to MEC.”
BackgroundAhmadi, M., Taghavirashidizadeh, A., Javaheri, D., Masoumian, A., Ghoushchi, S. J., & Pourasad, Y. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.08.019 · Cited by 70
“The placement of authentication-aware services at the Edge computing level has been studied to improve the Quality of Services (QoS) criteria based on FL in Qian et al. (2019).”
BackgroundIntegration of federated learning with IoT for smart cities applications, challenges, and solutions
Ghadi, Y. Y., Mazhar, T., Shah, S. F. A., Haq, I., Ahmad, W., Ouahada, K., & Hamam, H. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1657 · Cited by 71
“Edge devices have limited resources for memory storage, computational power, and information access ( Qian et al., 2019 ).”
BackgroundBlockchain-Enabled Asynchronous Federated Learning in Edge Computing
Liu, Y., Qu, Y., Xu, C., Hao, Z., & Gu, B. (2021) · Sensors · doi:10.3390/s21103335 · Cited by 65
“In [23], the authors designed a privacy-aware service placement (PSP) scheme in an edge-cloud system, which efficiently addressed individual privacy issues and provided better QoS to users.”
BackgroundA Systematic Survey of Blockchained Federated LearningPreprint
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“2) Edge Nodes: In [81]–[83], FL technology is used to support edge computing.”
MethodA Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems
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A survey of privacy-preserving offloading methods in mobile-edge computing
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Optimization of collaborative resource allocation for mobile edge computing
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AI-enabled mobile multimedia service instance placement scheme in mobile edge computing
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Federal learning edge network based sentiment analysis combating global COVID-19
Liang, W., Chen, X., Huang, S., Xiong, G., Yan, K., & Zhou, X. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.03.009 · Cited by 49
“HASSAN et al. [32] proposed a three-way federal averaging protocol in 2019, which has achieved certain results in reducing communication costs and improving training performance.”
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Privacy-aware offloading for training tasks of generative adversarial network in edge computing
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“Most existing studies either focused on the energy consumption of UEs [52], [60], [63] or assumed that UEs are always available to participate in FL [40], [42], [54].”
BackgroundSurvey on Federated-Learning Approaches in Distributed Environment
Gupta, R., & Alam, T. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-09624-y · Cited by 43
“On the basis of the outcomes of the study, PSP-Service-aware service-placement scheme through federated learning not only preserve the user’s data privacy and also minimizes the service load in the remote cloud [30].”
MethodDistributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions
Tu, J., Yang, L., & Cao, J. (2024) · ACM Computing Surveys · doi:10.1145/3708495 · Cited by 48
“To provide a better quality of service to users, Qian et al. [120] uses federation learning to provide privacy-aware service placement for mobile edge computing under the constraints of storage capacity, computing power, uplink, and download bandwidth.”
Zheng, C., Liu, S., Huang, Y., Zhang, W., & Yang, L. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3189055 · Cited by 37
“ularity depends on the user interest, which is complicated and spatiotemporal varying and, thus, is unavailable in advance no matter which caching policy is applied [18].”
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Security, privacy, and robustness for trustworthy AI systems: A review
Saeed, M. M., & Alsharidah, M. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109643 · Cited by 45
Machine learning methods for service placement: a systematic review
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Service Caching and Computation Reuse Strategies at the Edge: A Survey
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“Cache Pollution [42], [28], [39], [93] Privacy Information [94], [5]*, [68]* * Describes challenges, but does not provide solutions Multi-tenant Scenarios [15], [5]*, [21]* * Describes challenges, but does not provide solutions Homomorphic Encryption (Full or Partial) [83], [84], [85], [98], [68] None speciically for…”
BackgroundCov-Fed: Federated learning-based framework for COVID-19 diagnosis using chest X-ray scans
Adjei-Mensah, I., Zhang, X., Agyemang, I. O., Yussif, S. B., Baffour, A. A., Cobbinah, B. M., Sey, C., Fiasam, L. D., Chikwendu, I. A., & Arhin, J. R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107448 · Cited by 38
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“Next, we propose a hybrid service placement algorithm combining the concentrated greedy algorithm and the distributed coupling learning [47].”
MethodQu, Y., Yu, S., Gao, L., Sood, K., & Xiang, Y. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3382952 · Cited by 26
“In [32], the authors design a privacy-aware service placement (PSP) scheme in an edge-cloud system, which efficiently addresses individual privacy issues.”
BackgroundMa, X., Zhao, J.-H., & Gong, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3107465 · Cited by 22
“According to the mechanism of distributed learning, we can obtain that the raw data generated by the terminals will not pass through the wireless networks, which benefits to latency-aware and privacy-aware services [6], [30].”
BackgroundHeralding the Future of Federated Learning Framework: Architecture, Tools and Future Directions
Das, S. K., & Bebortta, S. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377066 · Cited by 23
“Thus, federated learning offers a decentralized training approach which in turn shall fetch training data from these devices without hampering the privacy concern of the users [4].”
BackgroundA Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers
Alashaikh, A. S., Alanazi, E., & Al‐Fuqaha, A. (2021) · ACM Computing Surveys · doi:10.1145/3450517 · Cited by 22
A systematic review on federated learning system: a new paradigm to machine learning
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An Efficient Caching and Offloading Resource Allocation Strategy in Vehicular Social Networks
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“Many studies have focused on maximizing the use of computing resources by building edge server clusters to reduce computing overhead and latency [34], [35], [36].”
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“Distributed behavioral learning and inference techniques that can support decentralization across the Edge Cloud while preserving the privacy of the raw training data, such as Federated Learning and Decentralized Learning, have also been considered [46], [52].”
BackgroundHudson, N., Khamfroush, H., Baughman, M., Lucani, D. E., Chard, K., & Foster, I. T. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.03.035 · Cited by 19
Mobility-aware and Privacy-protecting QoS Optimization in Mobile Edge Networks
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“Qian et al. [17] propose a service placement strategy based on FL combined with user preferences, while protecting the sensitive historical data of users’ mobile devices.”
BackgroundMethodInfluentialEGC: Entropy-based gradient compression for distributed deep learning
Xiao, D., Mei, Y., Kuang, D., Chen, M., Guo, B., & Wu, W. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.121 · Cited by 19
Microservice Pre-Deployment Based on Mobility Prediction and Service Composition in Edge
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“PSP scheme is presented by authors, which can decrease the load of services on the remote cloud as well as protect the privacy of users [53].”
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“This idea has been applied in edge computing systems to derive computation offloading, service placement, and content caching policies [5], [16], [25].”
MethodMalhotra, R., Bansal, A., & Kessentini, M. (2024) · Cluster Computing · doi:10.1007/s10586-024-04266-0 · Cited by 15
“It has been used in wireless communication [18–21], healthcare systems [22–25], and mobile devices for recommending the services [26–28].”
Perses: A framework for the continuous evaluation of the QoS of distributed mobile applications
Laso, S., Berrocal, J., Fernández, P., Ruiz–Cortés, A., & Murillo, J. M. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101627 · Cited by 13
Ten Questions in Lifelog Mining and Information Recall
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“Besides, federated learning [7, 37, 39], training a shared statistical model with users’ private data without directly accessing the data, might be a solution to enhance user privacy.”
BackgroundManocha, A., Sood, S. K., & Bhatia, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20084-3 · Cited by 12
“Qian et al. [21] provides a comprehensive overview of Federated Learning in healthcare and highlights its potential benefits and challenges.”
On Renting Edge Resources for Service Hosting
Narayana, V. S. C. L., Moharir, S., & Karamchandani, N. (2021) · ACM Transactions on Modeling and Performance Evaluation of Computing Systems · doi:10.1145/3478433 · Cited by 14
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“For instance, Wang et al. [139] proposed an ‘‘In-Edge AI’’ framework which integrates FL with MEC for resource allocation optimisation, and Qian et al. [140] proposed a privacy-aware service placement scheme.”
Federated Learning for Decentralized Electricity Market Optimization: A Review and Research Agenda
Miller, T. I., Durlik, I., Kostecka, E., Kozlovska, P., & Nowak, A. (2025) · Energies · doi:10.3390/en18174682 · Cited by 10
Thea - a QoS, Privacy, and Power-aware Algorithm for Placing Applications on Federated Edges
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Federated Learning-Based Service Caching in Multi-Access Edge Computing System
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An Energy-Aware Multi-Criteria Federated Learning Model for Edge Computing
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Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services
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Artificial Intelligence Advancement for 6G Communication: A Visionary Approach
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Resource Scheduling in Edge Computing: A SurveyPreprint
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A Survey of Edge Computing Resource Allocation Strategies Based on Federated Learning
Ni, S., He, Y., Chen, L., Wang, Y., & Yu, F. (2023) · International Conference on Networking and Network Applications · doi:10.1109/nana60121.2023.00027 · Cited by 3
“Qian et al. optimized federated learning at the edge of the network through radio resource allocation and scheduling, and defined a new performance indicator, training efficiency, which can not only speed up the convergence, but also improve the accuracy of the training process [6].”
BackgroundLoading Cost-Aware Model Caching And Request Routing In Edge-enabled Wireless Sensor Networks
Yao, M., Chen, L., Wu, Y., & Wu, J. (2022) · The Computer Journal · doi:10.1093/comjnl/bxac088 · Cited by 6
Detection of Malicious Clients in Federated Learning Using Graph Neural Network
Sharma, A., & Marchang, N. (2025) · IEEE Access · doi:10.1109/access.2025.3565712 · Cited by 7
“This decentralized framework addresses critical privacy concerns and is particularly advantageous in sensitive domains such as healthcare [5], [6], [7], [8], [9], [10], [11], [12], [13], finance [14], [15], [16], [17], and edge computing [18], [19], [20], [21], [22], [23], [24].”
Pang, S., Fan, Z., Gui, H., He, X., Lu, Z. G., Zhao, S., & Chen, B. (2025) · Cluster Computing · doi:10.1007/s10586-025-05314-z · Cited by 5
“The base station acts as a relay receiving federated learning tasks from users, while edge nodes act as independent workers participating in tasks by training models using local data [30].”
International revenue share fraud prediction on the 5G edge using federated learning
Ferreira, L., Silva, L. O., Morais, F., Martins, C. M., Pires, P. M., Rodrigues, H., Cortez, P. C., & Pilastri, A. L. (2023) · Computing · doi:10.1007/s00607-023-01174-w · Cited by 5
Süzen, A. A., & Şimşek, M. A. (2020) · Namık Kemal Tıp Dergisi · doi:10.37696/nkmj.660762 · Cited by 5
A Method of Federated Learning Based on Blockchain
Xu, S., Liu, S., & He, G. (2021) · International Conference on Computer Science and Application Engineering (CSAE) · doi:10.1145/3487075.3487143 · Cited by 5
“[9-10] To solve the above problem, we need to combine other cuttingedge technology with the existing federal study framework for re-”
MethodAn Efficient Asynchronous Federated Learning Protocol for Edge Devices
Li, Q. H., Gao, Z., Sun, Y., Wang, Y. L., Wang, R., & Zhu, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3406634 · Cited by 4
“Some studies aim to mitigate the high-communication costs resulting from system heterogeneity by reducing the size of aggregated subsets [25], [26], and addressing communication delays [27], [28], [29], [30].”
BackgroundJoint Service Placement and Request Routing in Mobile Edge Computing Networks
Yuan, B., Guo, S., & Wang, Q. (2021) · International Conference on Advanced Computational Intelligence (ICACI) · doi:10.1109/icaci52617.2021.9435886 · Cited by 4
“…edges, without long communication delay caused by accessing the remote cloud, playing the full potentials of MEC requires an effective strategy for allocating limited edge cloud resources to satisfy service requests generated from users, which have been widely researched in last few years [3]–[5].”
BackgroundMethodEdge server placement for offloading real‐time statistics tasks of bus passenger
Xu, H., Guo, Z., Shang, Z., Zhao, J., & Dai, H. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4702 · Cited by 4
Rasool, M. A. E., Kumar, A. S., Islam, A., & Ahmed, M. N. (2024) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip62566.2024.10639042 · Cited by 4
“Algorithm Granularities Analyzed Parameters [2] Suggested an approach based on Federated Learning to develop preference models, ensuring that user data remains stored on their respective personal devices.”
BackgroundMethodTang, W., Yang, Y., Gao, D., Chen, J., Huang, S., & Chen, N. (2024) · Wireless Networks · doi:10.1007/s11276-024-03838-7 · Cited by 3
“[9] constructed a user preference model according to the user's contextual information and proposed a privacy-aware service placement method via federated learning.”
Efficient Service Entity Chain Placement in Mobile Edge Computing
Liang, Y., Ge, J., Zhang, S., Niu, C., Song, W., & Luo, B. (2020) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn50589.2020.00042 · Cited by 3
“Some other works [8, 9, 14, 19, 22, 23] towards the service entity placement and caching have also been carried out.”
BackgroundImproved YOLOV3 Surveillance Device Object Detection Method Based on Federated Learning
Li, H., Yin, K., Ji, X., Liu, Y., Huang, T., & Yin, G. (2022) · International Conference on Data-driven Optimization of Complex Systems (DOCS) · doi:10.1109/docs55193.2022.9967481 · Cited by 3
“[16] based on the FL framework through multi-task learning to detect user behavior habits, so as to help residents reduce physical harm.”
MethodFederated Learning with Blockchain: A Study of the Latest Decentralized Couple
Faridi, A. R., Hafeez, A., & Masood, F. (2022) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis56430.2022.10037647 · Cited by 3
“Some systems use FL for edge computing [33]–[35].”
MethodA Review on Federated Learning with a Focus on Security and Privacy
Mathew, A., & Panchami, V. (2024) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics61201.2024.10689882 · Cited by 3
“Instead, the training data remains on the user’s device, drastically reducing network bandwidth usage and energy consumption[10].”
BackgroundFed-Tra: Improving Accuracy of Deep Learning Model on Non-iid in Federated Learning
Xiao, W., Tang, X., Zhou, B., Wang, W., Dong, Y., Zang, L., Han, J., & Hu, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95384-3_49 · Cited by 2
Privacy Preservation in User Behavior Analysis for Mobile-Edge Computing
Deng, S., Zhang, J., & Zhang, L. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3472608 · Cited by 2
“In view of the user’s privacy and the limited resources of the edge cloud, Qian et al. [30] proposed a privacy-sensitive service placement (PSP) strategy, which can protect user privacy while providing the user with better service quality.”
Survey of Edge Computing Based on a Generalized Framework and Some Recommendation
Sun, Y., Zhang, B., & Luo, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59824-2_10 · Cited by 2
Federated Learning at Mobile Edge Networks: A Tutorial
Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., & Miao, C. (2022) · Wireless networks · doi:10.1007/978-3-031-07838-5_1 · Cited by 4
Perancangan Federated Learning Berbasis Homomorphic Encryption untuk Perangkat Internet of Things
Saputra, Y. M., Alfian, G., & Octava, M. Q. H. (2023) · Journal of Internet and Software Engineering · doi:10.22146/jise.v4i1.6378 · Cited by 2
A systematic review of the foundations, applications, and challenges of federated learning
Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1
Metadata
- Authors
- Qian, Y., Hu, L., Chen, J., Guan, X., Hassan, M. M., & Alelaiwi, A.
- Affiliations
- China University of Geosciences (CN)
- Huazhong University of Science and Technology (CN)
- Wuhan University (CN)
- Heilongjiang University (CN)
- King Saud University (SA)
- Corresponding author
- Hu, Long
- Chen, Jing
- Guan, Xin
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 505
- Pages
- 562-570
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2964219000
- Cited by
- 122
- References
- 37
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 22 · 2023: 17 · 2022: 26 · 2021: 24 · 2020: 13 · 2019: 1
- FWCI
- 8.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Sciences (publishedVersion)
- Funders
- King Saud University
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- mobile edge computing, federated learning, user privacy preservation, quality of service, 0-1 integer programming, greedy algorithm, edge resource allocation, delay-sensitive services
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