Privacy-aware service placement for mobile edge computing via federated learning

Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks

Xu, J., Chen, L., & Zhou, P. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8485977 · Cited by 605 · see citing works

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

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Privacy Protection for Preventing Data Over-Collection in Smart City

Li, Y., Dai, W., Ming, Z., & Meikang Qiu, A. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2470247 · Cited by 321 · see citing works

Energy Efficient Task Caching and Offloading for Mobile Edge Computing

Hao, Y., Chen, M., Hu, L., Hossain, M. S., & Ghoneim, A. (2018) · IEEE Access · doi:10.1109/access.2018.2805798 · Cited by 273 · see citing works

Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings

Lin, K., Chen, M., Deng, J., Hassan, M. M., & Fortino, G. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2543242 · Cited by 246 · see citing works

Cognitive Computing: Architecture, Technologies and Intelligent Applications

Chen, M., Herrera, F., & Hwang, K. (2018) · IEEE Access · doi:10.1109/access.2018.2791469 · Cited by 242 · see citing works

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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It's Hard to Share: Joint Service Placement and Request Scheduling in Edge Clouds with Sharable and Non-Sharable Resources

He, T., Khamfroush, H., Wang, S., Porta, T. L., & Stein, S. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00044 · Cited by 237 · see citing works

Smart Home Based on WiFi Sensing: A Survey

Jiang, H., Cai, C., Ma, X., Yang, Y., & Liu, J. (2018) · IEEE Access · doi:10.1109/access.2018.2812887 · Cited by 221 · see citing works

Service Entity Placement for Social Virtual Reality Applications in Edge Computing

Wang, L., Jiao, L., He, T., Li, J., & Mühlhäuser, M. (2018) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2018.8486411 · Cited by 219 · see citing works

Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Yang, P., Zhang, N., Zhang, S., Yu, L. C., Zhang, J., & Shen, X. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2870521 · Cited by 198 · see citing works

A Dynamic Service Migration Mechanism in Edge Cognitive Computing

Chen, M., Li, W., Fortino, G., Hao, Y., Hu, L., & Humar, I. (2019) · ACM Transactions on Internet Technology · doi:10.1145/3239565 · Cited by 185 · see citing works

Towards decentralized IoT security enhancement: A blockchain approach

Qian, Y., Jiang, Y., Chen, J., Zhang, Y., Song, J., Zhou, M. L., & Pustišek, M. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.08.021 · Cited by 183 · see citing works

Uncovering the Face of Android Ransomware: Characterization and Real-Time Detection

Chen, J., Wang, C., Zhao, Z., Chen, K., Du, R., & Ahn, G. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2787905 · Cited by 168 · see citing works

ULOOF: A User Level Online Offloading Framework for Mobile Edge Computing

Neto, J. L. D., Yu, S.-Y., Macedo, D. F., Nogueira, J. M. S., Langar, R., & Secci, S. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2815015 · Cited by 155 · see citing works

Audio-visual emotion fusion (AVEF): A deep efficient weighted approach

Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.003 · Cited by 155 · cited 1× · see citing works

EARS: Emotion-aware recommender system based on hybrid information fusion

Qian, Y., Zhang⋆, Y., Ma, X., Yu, H., & Peng, L. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.06.004 · Cited by 143 · see citing works

Resource allocation robustness in multi-core embedded systems with inaccurate information

Jiayin Li, A., Ming, Z., Meikang Qiu, A., Quan, G., Qin, X., & Chen, T. (2011) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2011.03.005 · Cited by 131 · see citing works

Label-less Learning for Emotion Cognition

Chen, M., & Hao, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929071 · Cited by 124 · see citing works

Selfish Decentralized Computation Offloading for Mobile Cloud Computing in Dense Wireless Networks

Jošilo, S., & Dán, G. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2829874 · Cited by 121 · see citing works

TIDE: Time-relevant deep reinforcement learning for routing optimization

Sun, P., Hu, Y., Lan, J., Tian, L., & Chen, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.04.014 · Cited by 118 · see citing works

Label-less Learning for Traffic Control in an Edge Network

Chen, M., Hao, Y., Lin, K., Yuan, Z., & Hu, L. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800110 · Cited by 115 · see citing works

ILLIA: Enabling <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Anonymity-Based Privacy Preserving Against Location Injection Attacks in Continuous LBS Queries

Zhao, P., Li, J., Zeng, F., Xiao, F., Wang, C., & Jiang, H. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2799545 · Cited by 112 · see citing works

Privacy Protection and Intrusion Avoidance for Cloudlet-Based Medical Data Sharing

Chen, M., Qian, Y., Chen, J., Hwang, K., Mao, S., & Hu, L. (2016) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2016.2617382 · Cited by 111 · see citing works

Informer homed routing fault tolerance mechanism for wireless sensor networks

Meikang Qiu, A., Ming, Z., Jiayin Li, A., Liu, J., Quan, G., & Zhu, Y. (2013) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2012.12.003 · Cited by 108 · see citing works

Opportunistic Task Scheduling over Co-Located Clouds in Mobile Environment

Chen, M., Hao, Y., Lai, C., Wu, D., Li, Y., & Hwang, K. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2589247 · Cited by 108 · see citing works

Wearable Affective Robot

Chen, M., Zhou, J., Tao, G., Yang, J., & Hu, L. (2018) · IEEE Access · doi:10.1109/access.2018.2877919 · Cited by 105 · see citing works

Data Allocation for Hybrid Memory With Genetic Algorithm

Meikang Qiu, A., Chen, Z., Niu, J., Zong, Z., Quan, G., Qin, X., & Yang, L. T. (2015) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2015.2398824 · Cited by 103 · see citing works

Adaptive Wireless Video Streaming Based on Edge Computing: Opportunities and Approaches

Wang, D., Peng, Y., Ma, X., Ding, W., Jiang, H., Chen, F., & Liu, J. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2828426 · Cited by 103 · see citing works

Online Resource Allocation, Content Placement and Request Routing for Cost-Efficient Edge Caching in Cloud Radio Access Networks

Pu, L., Jiao, L., Chen, X., Wang, L., Xie, Q., & Xu, J. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2844624 · Cited by 100 · see citing works

Cognitive information measurements: A new perspective

Chen, M., Hao, Y., Gharavi, H., & Leung, V. C. M. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.046 · Cited by 82 · see citing works

Localization Based on Social Big Data Analysis in the Vehicular Networks

Lin, K., Luo, J., Hu, L., Hossain, M. S., & Ghoneim, A. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2641467 · Cited by 80 · see citing works

Blind Filtering at Third Parties: An Efficient Privacy-Preserving Framework for Location-Based Services

Chen, J., He, K., Yuan, Q., Chen, M., Du, R., & Xiang, Y. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2811481 · Cited by 74 · see citing works

Green Video Transmission in the Mobile Cloud Networks

Lin, K., Song, J., Luo, J., Ji, W., Hossain, M. S., & Ghoneim, A. (2016) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2016.2539618 · Cited by 68 · see citing works

Red/LeD: An Asymptotically Optimal and Scalable Online Algorithm for Service Caching at the Edge

Zhao, T., Hou, I.-H., Wang, S., & Chan, K. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2844919 · Cited by 67 · see citing works

Batch Identification Game Model for Invalid Signatures in Wireless Mobile Networks

Chen, J., He, K., Yuan, Q., Xue, G., Du, R., & Wang, L. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2604820 · Cited by 51 · see citing works

Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense NetworksPreprint

Xu, J., Chen, L., & Zhou, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05868 · Cited by 24 · see citing works

Task Offloading and Replication for Vehicular Cloud Computing: A Multi-Armed Bandit ApproachPreprint

Chen, L., & Xu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.04575 · Cited by 2 · see citing works

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 · see citing works

A 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 · see citing works

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 · see citing works

A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

Yin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638 · see citing works

Resource 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 · see citing works

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 · see citing works

A federated learning system with enhanced feature extraction for human activity recognition

Xiao, Z., Xu, X., Xing, H., Song, F., Wang, X., & Zhao, B. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107338 · Cited by 251 · see citing works

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 · see citing works

Federated Learning for Edge Computing: A Survey

Brecko, A., Kajáti, E., Koziorek, J., & Zolotová, I. (2022) · Applied Sciences · doi:10.3390/app12189124 · Cited by 162 · see citing works

Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey

Shuja, 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 157 · see citing works

Federated 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 · see citing works

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 160 · see citing works

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 · see citing works

Hybrid 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 · see citing works

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 · see citing works

Resource 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 104 · see citing works

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 · see citing works

Security 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 94 · see citing works

A 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 91 · see citing works

Enabling 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 · see citing works

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 88 · see citing works

Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84 · see citing works

A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

Saha, S., Hota, A., Chattopadhyay, A. K., Nag, A., & Nandi, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10766-7 · Cited by 88 · see citing works

A review on federated learning towards image processing

Khokhar, F. A., Shah, J. H., Khan, M. A., Sharif, M. I., Tariq, U., & Kadry, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107818 · Cited by 80 · see citing works

A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology

Ogundokun, R. O., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2022) · Information · doi:10.3390/info13050263 · Cited by 74 · see citing works

DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering

Ahmadi, 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 · see citing works

Integration 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 66 · see citing works

Blockchain-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 · see citing works

A Systematic Survey of Blockchained Federated LearningPreprint

Wang, Z., Hu, Q., Xu, M., Zhuang, Y., Wang, Y., & Cheng, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02182 · Cited by 86 · see citing works

A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems

Ali, W., Kumar, R., Deng, Z., Wang, Y., & Shao, J. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab025 · Cited by 62 · see citing works

A survey of privacy-preserving offloading methods in mobile-edge computing

Li, T., He, X., Jiang, S., & Liu, J. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103395 · Cited by 60 · see citing works

Optimization of collaborative resource allocation for mobile edge computing

Lv, Z., & Qiao, L. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.07.022 · Cited by 55 · see citing works

AI-enabled mobile multimedia service instance placement scheme in mobile edge computing

Roy, P., Sarker, S. C., Razzaque, M. A., Hassan, M. M., AlQahtani, S. A., Aloi, G., & Fortino, G. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107573 · Cited by 52 · see citing works

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

Salimian, M., Ghobaei‐Arani, M., & Shahidinejad, A. (2021) · Software Practice and Experience · doi:10.1002/spe.2986 · Cited by 50 · see citing works

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 · see citing works

HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing

Xu, Z.-C., Zhao, D., Liang, W.-F., Rana, O., Zhou, P., Li, M.-C., Xu, W., Li, H., & Xia, Q.-F. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3218807 · Cited by 49 · see citing works

Joint Optimization of Resource Utilization and Load Balance with Privacy Preservation for Edge Services in 5G Networks

Xu, X., Liu, X., Xu, Z., Wang, C., Wan, S., & Yang, X. (2019) · Mobile Networks and Applications · doi:10.1007/s11036-019-01448-8 · Cited by 46 · see citing works

Privacy-aware offloading for training tasks of generative adversarial network in edge computing

Xu, X., Liu, X., Yin, X., Wang, S., Qi, Q., & Qi, L. (2020) · Information Sciences · doi:10.1016/j.ins.2020.04.026 · Cited by 45 · see citing works

Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

Xu, Z., Li, D., Liang, W., Xu, W., Xia, Q., Zhou, P., RANA, O. F., & Li, H. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3262829 · Cited by 40 · see citing works

Survey on Federated-Learning Approaches in Distributed Environment

Gupta, R., & Alam, T. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-09624-y · Cited by 39 · see citing works

Distributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions

Tu, J.-F., Yang, L., & Cao, J.-N. (2024) · ACM Computing Surveys · doi:10.1145/3708495 · Cited by 44 · see citing works

Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile-Edge Computing Networks

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 · see citing works

Computing Offloading Strategy in Mobile Edge Computing Environment: A Comparison between Adopted Frameworks, Challenges, and Future Directions

Zhou, S., Jadoon, W., & Khan, I. A. (2023) · Electronics · doi:10.3390/electronics12112452 · Cited by 37 · see citing works

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 41 · see citing works

Machine learning methods for service placement: a systematic review

Haddadha, P. K., Rezvani, M. H., Mollamotalebi, M., & Shankar, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10684-0 · Cited by 35 · see citing works

Service Caching and Computation Reuse Strategies at the Edge: A Survey

Barrios, C., & Kumar, M. J. (2023) · ACM Computing Surveys · doi:10.1145/3609504 · Cited by 34 · see citing works

Cov-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 34 · see citing works

Review on Offloading of Vehicle Edge Computing

Wang, M., Yi, H., Jiang, F., Lin, L., & Gao, M. (2022) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2022.0120 · Cited by 33 · see citing works

Blockchained Dual-Asynchronous Federated Learning Services for Digital Twin Empowered Edge-Cloud Continuum

Qu, Y., Yu, S., Gao, L., Sood, K., & Xiang, Y. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3382952 · Cited by 26 · see citing works

Joint Scheduling and Resource Allocation for Efficiency-Oriented Distributed Learning Over Vehicle Platooning Networks

Ma, X., Zhao, J.-H., & Gong, Y. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3107465 · Cited by 22 · see citing works

Heralding 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 · see citing works

A 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 · see citing works

A systematic review on federated learning system: a new paradigm to machine learning

Chaudhary, R. K., Kumar, R., & Saxena, N. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02257-6 · Cited by 24 · see citing works

An Efficient Caching and Offloading Resource Allocation Strategy in Vehicular Social Networks

Zhang, Y., Zhou, Y., Zhang, S., Gui, G., Adebisi, B., Gacanin, H., & Sari, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3332905 · Cited by 21 · see citing works

Federated Learning Enabled Edge Computing Security for Internet of Medical Things: Concepts, Challenges and Open Issues

Srivastava, G., Kandati, D. R., Supriya, Y., Yenduri, G., Hegde, P., Gadekallu, T. R., Maddikunta, P. K. R., & Bhattacharya, S. (2023) · Advances in information security · doi:10.1007/978-3-031-28150-1_3 · Cited by 20 · see citing works

A Framework for Cognitive, Decentralized Container Orchestration

Sofia, R. C., Salomon, J., Ferlin, S., Garcés-Erice, L., Urbanetz, P., MUELLER, H. S., Touma, R., Espinosa, A., Contreras, L. M., Theodorou, V., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3406861 · Cited by 20 · see citing works

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Hudson, 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 · see citing works

Mobility-aware and Privacy-protecting QoS Optimization in Mobile Edge Networks

Jin, H., Zhang, P., Dong, H., Wei, X., Zhu, Y., & Gu, T. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3230856 · Cited by 19 · see citing works

EGC: 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 · see citing works

Microservice Pre-Deployment Based on Mobility Prediction and Service Composition in Edge

Deng, J., Li, B., Wang, J., & Zhao, Y. (2021) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws53863.2021.00078 · Cited by 17 · see citing works

Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing

Jin, H., Zhang, P., Dong, H., Zhu, Y., & Bouguettaya, A. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3137452 · Cited by 17 · see citing works

A Systematic Review on Federated Learning in Edge-Cloud Continuum

Mishra, S. K., Sahoo, S. K., & Swain, C. K. (2024) · SN Computer Science · doi:10.1007/s42979-024-03235-z · Cited by 15 · see citing works

Seek Common While Shelving Differences: Orchestrating Deep Neural Networks for Edge Service Provisioning

Chen, L., & Xu, J. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036953 · Cited by 15 · see citing works

Deployment and performance monitoring of docker based federated learning framework for software defect prediction

Malhotra, R., Bansal, A., & Kessentini, M. (2024) · Cluster Computing · doi:10.1007/s10586-024-04266-0 · Cited by 14 · see citing works

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 · see citing works

Ten Questions in Lifelog Mining and Information Recall

Yen, A.-Z., Huang, H., & Chen, H. (2021) · International Conference on Multimedia Retrieval · doi:10.1145/3460426.3463607 · Cited by 13 · see citing works

Federated learning-inspired smart ECG classification: an explainable artificial intelligence approach

Manocha, A., Sood, S. K., & Bhatia, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20084-3 · Cited by 12 · see citing works

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 · see citing works

A Comprehensive Review of Cryptographic Techniques in Federated Learning for Secure Data Sharing and Applications

Sen, A., Heng, S., & Tan, S. C. (2025) · IEEE Access · doi:10.1109/access.2025.3593953 · Cited by 12 · see citing works

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 · see citing works

Thea - a QoS, Privacy, and Power-aware Algorithm for Placing Applications on Federated Edges

de Souza, P. S. S., Kayser, C., Roges, L., & Ferreto, T. C. (2023) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp59025.2023.00028 · Cited by 10 · see citing works

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Tran, T. P., Tran, A. H. N., Nguyen, T. M., & Yoo, M. (2024) · Applied Sciences · doi:10.3390/app14010401 · Cited by 10 · see citing works

An Energy-Aware Multi-Criteria Federated Learning Model for Edge Computing

Al-Saedi, A. A., Casalicchio, E., & Boeva, V. (2021) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud49777.2021.00027 · Cited by 9 · see citing works

Ensemble Deep Learning Assisted VNF Deployment Strategy for Next-Generation IoT Services

Emu, M., & Choudhury, S. (2021) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2021.3098462 · Cited by 9 · see citing works

FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection

Nguyen, T., Nguyen, A. D., Nguyen, T.-H., Wong, K., Pham, H., Nguyen, T. T., & Nguyen, P. L. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191655 · Cited by 8 · see citing works

The role of mobile edge computing in advancing federated learning algorithms and techniques: A systematic review of applications, challenges, and future directions

Rahmani, A. M., Alsubai, S., Alanazi, A., Alqahtani, A., Zaidi, M. M., & Hosseinzadeh, M. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109812 · Cited by 7 · see citing works

Artificial Intelligence Advancement for 6G Communication: A Visionary Approach

Miya, J., Raj, S., Ansari, M. A., Kumar, S. M., & Kumar, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-30101-8_15 · Cited by 7 · see citing works

Resource Scheduling in Edge Computing: A SurveyPreprint

Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.08059 · Cited by 7 · see citing works

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 · see citing works

Loading 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 · see citing works

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 5 · see citing works

Reputation-based federated learning and blockchain for trustworthy service recommendations in edge computing

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 · see citing works

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 · see citing works

Sağlık Alanında Veri Mahremiyetinin Korunmasına Yönelik Makine Öğrenmesi Uygulamalarına Yeni Bir Yaklaşım: Federe Öğrenme

Süzen, A. A., & Şimşek, M. A. (2020) · Namık Kemal Tıp Dergisi · doi:10.37696/nkmj.660762 · Cited by 5 · see citing works

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 · see citing works

An 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 · see citing works

Joint 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 · see citing works

Edge 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 · see citing works

Exploring Task Offloading in Mobile Edge Computing Environments: An In-Depth Review and Prospective Analysis

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 · see citing works

An optimized task offloading strategy based on deep reinforcement learning combined with channel reliability prediction

Tang, W., Yang, Y., Gao, D., Chen, J., Huang, S., & Chen, N. (2024) · Wireless Networks · doi:10.1007/s11276-024-03838-7 · Cited by 3 · see citing works

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 · see citing works

Improved 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 · see citing works

Federated 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 · see citing works

A 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 · see citing works

Fed-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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

PrivacyAware Federated Edge Server Placement for Socially Collaborative Mobile Applications

Asghari, A., Mojahedivaraki, M., & Barzegarinezhad, A. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090320

Cooperative Service Placement and Scheduling in Edge Clouds: A Deadline-Driven Approach

Li, Y., Dai, W., Gan, X., Jin, H., Fu, L., Ma, H., & Wang, X. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3061602 · Cited by 54 · see citing works

Privacy-Preserving QoS Forecasting in Mobile Edge Environments

Zhang, P., Jin, H., Dong, H., Song, W., & Bouguettaya, A. (2020) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2020.2977018 · Cited by 23 · see citing works

PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing

Xu, X., Liu, W., Zhang, Y., Zhang, X., Dou, W., Qi, L., & Bhuiyan, M. Z. A. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501810 · Cited by 61 · see citing works

Adaptive joint placement of edge intelligence services in mobile edge computing

Du, L., Huo, R., Sun, C., Wang, S., & Huang, T. (2023) · Wireless Networks · doi:10.1007/s11276-023-03520-4 · Cited by 13 · see citing works