Smart City Gnosys
Smart city article details
| Title | Decentralized Federated Learning Methods For Reducing Communication Cost And Energy Consumption In Uav Networks |
|---|---|
| ID_Doc | 17651 |
| Authors | Pan D.; Khoshkholghi M.A.; Mahmoodi T. |
| Year | 2023 |
| Published | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 495 LNICST |
| DOI | http://dx.doi.org/10.1007/978-3-031-31891-7_2 |
| Abstract | Unmanned aerial vehicles (UAV) or drones play many roles in a modern smart city such as the delivery of goods, mapping real-time road traffic and monitoring pollution. The ability of drones to perform these functions often requires the support of machine learning technology. However, traditional machine learning models for drones encounter data privacy problems, communication costs and energy limitations. Federated Learning, an emerging distributed machine learning approach, is an excellent solution to address these issues. Federated learning (FL) allows drones to train local models without transmitting raw data. However, existing FL requires a central server to aggregate the trained model parameters of the UAV. A failure of the central server can significantly impact the overall training. In this paper, we propose two aggregation methods: Commutative FL and Alternate FL, based on the existing architecture of decentralised Federated Learning for UAV Networks (DFL-UN) by adding a unique aggregation method of decentralised FL. Those two methods can effectively control energy consumption and communication cost by controlling the number of local training epochs, local communication, and global communication. The simulation results of the proposed training methods are also presented to verify the feasibility and efficiency of the architecture compared with two benchmark methods (e.g. standard machine learning training and standard single aggregation server training). The simulation results show that the proposed methods outperform the benchmark methods in terms of operational stability, energy consumption and communication cost. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. |
| Author Keywords | Decentralized Training; Federated Learning; Unmanned Aerial vehicles |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
59307  | 0.894 | Aqleem Abbas S.M.; Khattak M.A.K.; Boulila W.; Kouba A.; Shahbaz Khan M.; Ahmad J. | Uavs And Blockchain Synergy: Enabling Secure Reputation-Based Federated Learning In Smart Cities | IEEE Access, 12 (2024) |
| 26346 | 0.879 | Nguyen D.C.; Ding M.; Pathirana P.N.; Seneviratne A.; Li J.; Vincent Poor H. | Federated Learning For Internet Of Things: A Comprehensive Survey | IEEE Communications Surveys and Tutorials, 23, 3 (2021) |
| 23748 | 0.877 | Gomes D.; Hasan M.; Philip S.R. | Enhancing Capabilities And Security Features Of Drone Networks Through Machine Learning: A Comprehensive Overview | Advances in Science, Technology and Innovation, Part F372 (2025) |
| 1161 | 0.866 | Kishor K.; Quezada A.; Yadav S.P. | A Critical Role For Federated Learning In Iot | Federated Learning for Smart Communication using IoT Application (2024) |
| 16204 | 0.863 | Qin C.; Pournaras E. | Coordination Of Drones At Scale: Decentralized Energy-Aware Swarm Intelligence For Spatio-Temporal Sensing | Transportation Research Part C: Emerging Technologies, 157 (2023) |
| 26356 | 0.859 | Valente R.; Senna C.; Rito P.; Sargento S. | Federated Learning Framework To Decentralize Mobility Forecasting In Smart Cities | Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023 (2023) |
| 23481 | 0.858 | Wang Y.; Sui M.; Xia T.; Liu M.; Yang J.; Zhao H. | Energy-Efficient Federated Learning-Driven Intelligent Traffic Monitoring: Bayesian Prediction And Incentive Mechanism Design | Electronics (Switzerland), 14, 9 (2025) |
| 16402 | 0.858 | Wang D.; Wu M.; Chakraborty C.; Min L.; He Y.; Guduri M. | Covert Communications In Air-Ground Integrated Urban Sensing Networks Enhanced By Federated Learning | IEEE Sensors Journal, 24, 5 (2024) |
| 26338 | 0.857 | Quan M.K.; Pathirana P.N.; Wijayasundara M.; Setunge S.; Nguyen D.C.; Brinton C.G.; Love D.J.; Poor H.V. | Federated Learning For Cyber Physical Systems: A Comprehensive Survey | IEEE Communications Surveys and Tutorials (2025) |
| 35928 | 0.855 | Kim K.; Park Y.M.; Seon Hong C. | Machine Learning Based Edge-Assisted Uav Computation Offloading For Data Analyzing | International Conference on Information Networking, 2020-January (2020) |