Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems
The use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as key enablers of new applications, including: surveillance and monitoring, military, delivery of medical supplies, telecommunications, etc. In particular, due to their unique proprieties such as flexibility, mobili...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9039589/ |
| _version_ | 1818323655598276608 |
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| author | Bouziane Brik Adlen Ksentini Maha Bouaziz |
| author_facet | Bouziane Brik Adlen Ksentini Maha Bouaziz |
| author_sort | Bouziane Brik |
| collection | DOAJ |
| description | The use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as key enablers of new applications, including: surveillance and monitoring, military, delivery of medical supplies, telecommunications, etc. In particular, due to their unique proprieties such as flexibility, mobility, and adaptive altitude, UAVs can act as mobile base stations to improve capacity, coverage, and energy efficiency of wireless networks. On the other hand, UAVs can operate as mobile terminals to enable many applications such as item delivery and real-time video streaming. In such context, data-driven Deep Learning-assisted (DL) approaches are gaining a growing interest to not only exploit the huge amount of generated data, but also to optimize the network operations, and hence ensure the QoS requirements of these emerging wireless networks. However, UAVs are resource-constrained devices especially in terms of computing and power resources, and traditional DL-assisted schemes are cloud-centric, which require UAVs' data to be sent and stored in a centralized server. This represents a critical issue since it generates a huge network communication overhead to send raw data towards the centralized entity, and hence may lead to network bandwidth and energy inefficiency of UAV devices. In addition, the transferred data may contain personnel data such as UAVs' localization and identity, which can directly affect UAVs' privacy concerns. As a solution, Federated Deep Learning (FDL), or distributed DL, was introduced, where the basic idea is to keep raw data where it is generated, while sending only users' local trained DL models to the centralized entity for aggregation. Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications. In this work, we provide a general introduction of FDL application for UAV-enabled wireless networks. We first introduce the FDL concept and its fundamentals. Then, we highlight the possible applications of FDL in UAVs-enabled wireless networks by addressing the suitability and how to use FDL to deal with target challenges. Finally, we discuss about key technical challenges, open issues, and future research directions on FDL-based approaches in such context. |
| first_indexed | 2024-12-13T11:16:09Z |
| format | Article |
| id | doaj.art-3672dcf466754b93b8fc6634e26e3aa2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:16:09Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3672dcf466754b93b8fc6634e26e3aa22022-12-21T23:48:37ZengIEEEIEEE Access2169-35362020-01-018538415384910.1109/ACCESS.2020.29814309039589Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open ProblemsBouziane Brik0https://orcid.org/0000-0002-3267-5702Adlen Ksentini1Maha Bouaziz2EURECOM, Biot, FranceEURECOM, Biot, FranceEURECOM, Biot, FranceThe use of Unmanned Aerial Vehicles (UAVs) for wireless networks is rapidly growing as key enablers of new applications, including: surveillance and monitoring, military, delivery of medical supplies, telecommunications, etc. In particular, due to their unique proprieties such as flexibility, mobility, and adaptive altitude, UAVs can act as mobile base stations to improve capacity, coverage, and energy efficiency of wireless networks. On the other hand, UAVs can operate as mobile terminals to enable many applications such as item delivery and real-time video streaming. In such context, data-driven Deep Learning-assisted (DL) approaches are gaining a growing interest to not only exploit the huge amount of generated data, but also to optimize the network operations, and hence ensure the QoS requirements of these emerging wireless networks. However, UAVs are resource-constrained devices especially in terms of computing and power resources, and traditional DL-assisted schemes are cloud-centric, which require UAVs' data to be sent and stored in a centralized server. This represents a critical issue since it generates a huge network communication overhead to send raw data towards the centralized entity, and hence may lead to network bandwidth and energy inefficiency of UAV devices. In addition, the transferred data may contain personnel data such as UAVs' localization and identity, which can directly affect UAVs' privacy concerns. As a solution, Federated Deep Learning (FDL), or distributed DL, was introduced, where the basic idea is to keep raw data where it is generated, while sending only users' local trained DL models to the centralized entity for aggregation. Due to its privacy-preserving and low communication overhead and latency, FDL is much more adequate for many UAVs-enabled wireless applications. In this work, we provide a general introduction of FDL application for UAV-enabled wireless networks. We first introduce the FDL concept and its fundamentals. Then, we highlight the possible applications of FDL in UAVs-enabled wireless networks by addressing the suitability and how to use FDL to deal with target challenges. Finally, we discuss about key technical challenges, open issues, and future research directions on FDL-based approaches in such context.https://ieeexplore.ieee.org/document/9039589/Deep learningfederated deep learningUAVs-based wireless networkswireless communications |
| spellingShingle | Bouziane Brik Adlen Ksentini Maha Bouaziz Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems IEEE Access Deep learning federated deep learning UAVs-based wireless networks wireless communications |
| title | Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems |
| title_full | Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems |
| title_fullStr | Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems |
| title_full_unstemmed | Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems |
| title_short | Federated Learning for UAVs-Enabled Wireless Networks: Use Cases, Challenges, and Open Problems |
| title_sort | federated learning for uavs enabled wireless networks use cases challenges and open problems |
| topic | Deep learning federated deep learning UAVs-based wireless networks wireless communications |
| url | https://ieeexplore.ieee.org/document/9039589/ |
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