The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
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)
Citations
Cites
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
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications
(2018) · doi:10.1109/infocom36044.2018 · Cited by 379
Privacy Protection for Preventing Data Over-Collection in Smart City
Li, Y., Dai, W., Ming, Z., & Qiu, M. (2015) · IEEE Transactions on Computers · doi:10.1109/tc.2015.2470247 · Cited by 329
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
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
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
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
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
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
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Items 101–122 of 122
Privacy-Preserving Federated Learning in Oil Palm Industry for Fresh Fruit Bunch Ripeness Grading
Laddawong, P., Pianroj, Y., Chotikawanid, P., Punvichai, T., Jumrat, S., & Muangprathub, J. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.3.434-446 · Cited by 1
“[24] presented an overview of FL, discussing distributed machine learning, privacy considerations, and categorization of FL architecture, FL design, and applications of FL.”
HeaPS: Heterogeneity-aware participant selection for efficient federated learning
Yang, D., Hu, B., Gao, Y., Jin, A.-L., Liu, A., Yeung, K. L., & You, Y. (2025) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2025.105168 · Cited by 1
Dogruluk, E., & Açıkgöz, H. (2025) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia67127.2025.11200757 · Cited by 1
“Qian et al. [17] propose an FL framework adapted for constrained devices, combining secure aggregation with differential privacy to reduce leakage while preserving model performance [23].”
Metaverse Service Provisioning Empowered by Monitoring and Analytical Digital Twins in MEC
Xu, G., Xu, Z., Xia, Q., Yan, B., Zhen, F., & Xu, P. (2025) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc67675.2025.00083 · Cited by 1
“The computing resource capacity on each base station is randomly drawn in [1000, 2500] MHz [24], [35].”
Toward Efficient Collaborative Inference in IIoT: A Diffusion Model and DRL-Based Approach
Chen, Y., Zhou, J., Liu, B., & Niu, X. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3669635 · Cited by 1
“…[70], unified frameworks integrating sampling with task offloading and computation scheduling [71], [72], [73], [74], predictive maintenance and anomaly detection applications [75], and privacy and system-aware designs for digital twin or federated IIoT architectures [76], [77], [78], [79], [80].”
Systematic Review of Privacy Preservation in Federated Learning for Secured Healthcare Applications
Alankamony, A., & Nels, N. (2026) · Information · doi:10.3390/info17070647 · Cited by 2
Spatio-Temporal Federated Learning for Massive Wireless Edge Networks
Liu, C.-H., Feng, K.-T., Wei, L.-W., & Luo, Y.-J. (2021) · ICC 2022 - IEEE International Conference on Communications · doi:10.1109/icc45855.2022.9838401 · Cited by 1
“The study of FL over wireless networks is still in its infancy, yet it can be categorized into three main directions: over-the-air digital and analog data aggregation, communication and computation efficiency, privacy and security [3]–[12].”
BackgroundOverview of Federated Learning and Its Advantages
Kakkar, A., & Kumar, S. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-1874-4.ch016 · Cited by 1
A Review: Scheduling Methods in Serverless Edge Computing
Ebrahem, A. T., & Mahmmood, B. S. (2023) · International Research Journal of Innovations in Engineering and Technology · doi:10.47001/irjiet/2023.702005 · Cited by 1
“In addition to, Qian and his colleagues [35] presented hybrid method from federate learning model and a centralized greedy technique for tackling the issue of functions placement with kept on privacy in the fog computing.”
MethodLi, C., Zhang, Y., & Luo, Y. (2022) · Research Square · doi:10.21203/rs.3.rs-821834/v1 · Cited by 1
Proposed Data Sanitization for Privacy Preservation in Mobile Computing
Awasthi, S., & Rizvi, S. W. A. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2022.2145661
“MCC is a kind of cloud computing that combines mobile phones, the internet, and the cloud (Qian et al. 2019; Stephanie et al. 2022; Saharan et al. 2021; Ahamad et al. 2020).”
Survey on Multiple Methods for Task Offloading in Mobile Cloud-Edge Environment
Tan, R., Longxin, Z., Ai, M., Zeng, W., & Chen, J. (2023) · Computer Science and Technology · doi:10.57237/j.cst.2023.03.001
“图 3 融合 LSTM 与 DuelingDQN 的三层感知机 http://www.computscitech.com Tan 等人[48]为了解决 MEC 网络中的多用户协同 卸载和资源分配的问题并实现能耗最小化，提出了一 种基于蚁群系统（Ant Colony System, ACS）与 DQN 的联合迭代算法（Joint Iterative Algorithm, JIA）。多用 户协同计算即任务除了能在本地计算或者边缘节点计 算外，还能传输给其他终端协同计算。JIA 中，模型上 层使用启发式算法来处理已设置的协作决策和卸载决 策；在底层中，通过基于当前卸载决策的深度强化学 习来更新功率、子载波和计算资源的分配。通过实验 结果…”
InfluentialA Review of Privacy-Preserving Decentralized Learning
Gitanshu, & Kumar, N. B. M. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_29
Federated Learning: Concepts, Application and Future Scope
Jain, P., & Patidar, S. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-5200-3_2
Liu, H., Xu, Z., Di, X., Su, J., & Che, X. (2025) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa67752.2025.00055
“For service provision, Qian et al. [14] proposed a privacy-aware provisioning framework combining a centralized greedy algorithm with distributed federated learning, enabling both privacy protection and efficient service delivery through mobile edge clouds.”
Shala, E., Brunner, J. O., Vehreschild, J. J., Heller, A. R., & Bartenschlager, C. C. (2026) · Flexible Services and Manufacturing Journal · doi:10.1007/s10696-025-09646-1
Deploying Privacy-Preserving Mechanisms for Financial Applications: A Comprehensive Survey
Wang, Z., Wang, S., & Zhao, P. (2025) · International Conference on Cyber Security and Information Engineering (ICCSIE) · doi:10.1109/iccsie65832.2025.00039
“In such a scenario, Zhao et al. & Qian et al. [18,19] investigate the privacy-preserving offloading schemes where location privacy and the usage pattern privacy are taken into consideration.”
GenioSim: A novel simulation platform for edge computing over optical networks
Cesarano, C., Foggia, A., & Natella, R. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108578
“…, where clients upload and execute individual tasks on edge computing servers: however, more complex business service models deploy long-running applications on edge servers ( service placement ), to reduce response latency and share resources among multiple service requests [13, 14, 15, 16].”
Secure and Cost-Efficient Microservice Placement in MEC Through Network-Aware Adaptation
He, F., Bujari, A., Bellavista, P., Li, P., Xiao, Z., Romandini, N., & Qiu, Y. F. (2026) · ACM Transactions on Internet Technology · doi:10.1145/3815782
“Privacy-aware placement has received even less attention [32, 41].”
InfluentialXu, W., Tang, B., Zhang, L., Cao, B., Yang, Q., & Li, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3720543
“Qian et al. [34] designed a privacy-aware service placement scheme to handle privacy-conscious placement for mobile users.”
Divyansh, S. (2026) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/26oct866
“Model training, when used, exchanges only model weights [6], [15]; the telemetry itself could additionally be protected with authenticated aggregation [16].”
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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