Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process
Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Feder...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/14/2119 |
| _version_ | 1797406745444745216 |
|---|---|
| author | Sofiane Dahmane Mohamed Bachir Yagoubi Bouziane Brik Chaker Abdelaziz Kerrache Carlos Tavares Calafate Pascal Lorenz |
| author_facet | Sofiane Dahmane Mohamed Bachir Yagoubi Bouziane Brik Chaker Abdelaziz Kerrache Carlos Tavares Calafate Pascal Lorenz |
| author_sort | Sofiane Dahmane |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process. |
| first_indexed | 2024-03-09T03:31:02Z |
| format | Article |
| id | doaj.art-f3c8f45285284ef287fcad6b4a587ba4 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T03:31:02Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f3c8f45285284ef287fcad6b4a587ba42023-12-03T14:56:46ZengMDPI AGElectronics2079-92922022-07-011114211910.3390/electronics11142119Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning ProcessSofiane Dahmane0Mohamed Bachir Yagoubi1Bouziane Brik2Chaker Abdelaziz Kerrache3Carlos Tavares Calafate4Pascal Lorenz5Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, AlgeriaLaboratoire d’Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, AlgeriaDRIVE EA1859, University Bourgogne Franche-Comté, 58000 Nevers, FranceLaboratoire d’Informatique et de Mathématiques, Université Amar Telidji, Laghouat 03000, AlgeriaComputer Engineering Department (DISCA), Universitat Politècnica de València, 46022 Valencia, SpainGRTC Laboratory, University of Haute Alsace, 68008 Colmar, FranceUnmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process.https://www.mdpi.com/2079-9292/11/14/2119UAV networksFederated Deep LearningUAVs selectionblockchainedge computing |
| spellingShingle | Sofiane Dahmane Mohamed Bachir Yagoubi Bouziane Brik Chaker Abdelaziz Kerrache Carlos Tavares Calafate Pascal Lorenz Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process Electronics UAV networks Federated Deep Learning UAVs selection blockchain edge computing |
| title | Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process |
| title_full | Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process |
| title_fullStr | Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process |
| title_full_unstemmed | Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process |
| title_short | Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process |
| title_sort | multi constrained and edge enabled selection of uav participants in federated learning process |
| topic | UAV networks Federated Deep Learning UAVs selection blockchain edge computing |
| url | https://www.mdpi.com/2079-9292/11/14/2119 |
| work_keys_str_mv | AT sofianedahmane multiconstrainedandedgeenabledselectionofuavparticipantsinfederatedlearningprocess AT mohamedbachiryagoubi multiconstrainedandedgeenabledselectionofuavparticipantsinfederatedlearningprocess AT bouzianebrik multiconstrainedandedgeenabledselectionofuavparticipantsinfederatedlearningprocess AT chakerabdelazizkerrache multiconstrainedandedgeenabledselectionofuavparticipantsinfederatedlearningprocess AT carlostavarescalafate multiconstrainedandedgeenabledselectionofuavparticipantsinfederatedlearningprocess AT pascallorenz multiconstrainedandedgeenabledselectionofuavparticipantsinfederatedlearningprocess |