Smart City Gnosys
Smart city article details
| Title | Edge-Assisted Federated Learning Framework For Smart Crowd Management |
|---|---|
| ID_Doc | 21817 |
| Authors | Siddiqa A.; Khan W.Z.; Alkinani M.H.; Aldhahri E.A.; Khan M.K. |
| Year | 2024 |
| Published | Internet of Things (Netherlands), 27 |
| DOI | http://dx.doi.org/10.1016/j.iot.2024.101253 |
| Abstract | Crowd management is a complex issue that requires a multifaceted approach. It entails employing various strategies and techniques to manage large crowds in urban areas effectively. Crowd management's overall goal is to ensure the safety and security of the crowd and the surrounding community, minimizing disruptions and ensuring the smooth flow of people and traffic. There could be two main challenges in smart crowd management in a smart city environment, i.e., real-time processing of large magnitude of data and privacy preservation. Edge computing devices can be used to process large amounts of data, and privacy concerns can be addressed with the help of federated learning. To address the aforementioned challenges, in this paper, we have proposed an edge-assisted four-layered conceptual framework (i.e., data acquisition, edge computing, cloud computing, and application layers) using federated learning. Practical implications and their potential solutions using the proposed framework are suggested. To fully utilize the benefits of edge-assisted federated learning for crowd management within smart cities, various research challenges are highlighted that need to be addressed. Moreover, a case study in the context of smart cities is presented to highlight the potential benefits of the proposed framework. Simulation results for federated learning-based anomaly detection using three to five local clients are also presented as well to validate the effectiveness of the proposed framework. The effect of label poisoning attacks on FL-based anomaly detection is also discussed. © 2024 Elsevier B.V. |
| Author Keywords | Crowd management; Edge computing; Federated learning; Internet of Things; Smart cities; Wearable technology |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
26364  | 0.881 | Liu D.; Cui E.; Shen Y.; Ding P.; Zhang Z. | Federated Learning Model Training Mechanism With Edge Cloud Collaboration For Services In Smart Cities | IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, 2023-June (2023) |
| 25437 | 0.878 | Albaseer A.; Ciftler B.S.; Abdallah M.; Al-Fuqaha A. | Exploiting Unlabeled Data In Smart Cities Using Federated Edge Learning | 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 (2020) |
| 26340 | 0.878 | Hamid S.; Bawany N.Z. | Federated Learning For Enhanced Intrusion Detection In Smart City Environments | 2024 18th International Conference on Open Source Systems and Technologies, ICOSST 2024 - Proceedings (2024) |
| 26359 | 0.878 | Gandhi M.; Singh S.K.; Ravikumar R.N.; Vaghela K. | Federated Learning In Secure Smart City Sensing: Challenges And Opportunities | Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025) |
| 26330 | 0.874 | Janaki G.; Umanandhini D. | Federated Learning Approaches For Decentralized Data Processing In Edge Computing | Proceedings of the 5th International Conference on Smart Electronics and Communication, ICOSEC 2024 (2024) |
| 26337 | 0.874 | Pang Y.; Ni Z.; Zhong X. | Federated Learning For Crowd Counting In Smart Surveillance Systems | IEEE Internet of Things Journal, 11, 3 (2024) |
| 26332 | 0.871 | Jiang Y.; Cong R.; Shu C.; Yang A.; Zhao Z.; Min G. | Federated Learning Based Mobile Crowd Sensing With Unreliable User Data | Proceedings - 2020 IEEE 22nd International Conference on High Performance Computing and Communications, IEEE 18th International Conference on Smart City and IEEE 6th International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020 (2020) |
| 46998 | 0.87 | Mishra S.K.; Kumar N.S.; Rao B.; Brahmendra; Teja L. | Role Of Federated Learning In Edge Computing: A Survey | Journal of Autonomous Intelligence, 7, 1 (2024) |
| 924 | 0.869 | Mehdi M.; Makkar A.; Conway M. | A Comprehensive Review Of Open-Source Federated Learning Frameworks | Procedia Computer Science, 260 (2025) |
| 23947 | 0.869 | Kapoor A.; Kumar D. | Enhancing Smart Cities With Federated Learning: A Framework For Secure, Scalable, And Intelligent Urban Sensing Systems | IEEE Internet of Things Magazine (2025) |