ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network

Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm, facilitating knowledge sharing among distributed edge devices while upholding data privacy. However, dynamic networks and resource-constrained devices such as drones, face challenges like power outages and netw...

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Published in:	International journal of machine learning and cybernetics Vol. 15; no. 11; pp. 5303 - 5319
Main Authors:	Ihekoronye, Vivian Ukamaka, Nwakanma, Cosmas Ifeanyi, Kim, Dong-Seong, Lee, Jae Min
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial Intelligence Clients Communication Complex Systems Computational Intelligence Control Cybersecurity Deep learning Design Drones Effectiveness Efficiency Engineering Federated learning Flexibility Learning Machine learning Mechatronics Optimization techniques Original Article Pattern Recognition Privacy Robotics Systems Biology Wireless networks Semi-asynchronous technique Cybersecurity Federated learning Straggler effect Intrusion detection Drone security networks
ISSN:	1868-8071, 1868-808X
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Abstract	Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm, facilitating knowledge sharing among distributed edge devices while upholding data privacy. However, dynamic networks and resource-constrained devices such as drones, face challenges like power outages and network contingencies, leading to the straggler effect that impedes the global model performance. To address this, we present ASR-Fed, a novel agnostic straggler-resilient semi-asynchronous FL aggregating algorithm. ASR-Fed incorporates a selection function to dynamically utilize updates from high-performing and active clients, while circumventing contributions from straggling clients during future aggregations. We evaluate the effectiveness of ASR-Fed using two prominent cyber-security datasets, WSN-DS, and Edge-IIoTset, and perform simulations with different deep learning models across formulated unreliable network scenarios. The simulation results demonstrate ASR-Fed’s effectiveness in achieving optimal accuracy while significantly reducing communication costs when compared with other FL aggregating protocols.
AbstractList	Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm, facilitating knowledge sharing among distributed edge devices while upholding data privacy. However, dynamic networks and resource-constrained devices such as drones, face challenges like power outages and network contingencies, leading to the straggler effect that impedes the global model performance. To address this, we present ASR-Fed, a novel agnostic straggler-resilient semi-asynchronous FL aggregating algorithm. ASR-Fed incorporates a selection function to dynamically utilize updates from high-performing and active clients, while circumventing contributions from straggling clients during future aggregations. We evaluate the effectiveness of ASR-Fed using two prominent cyber-security datasets, WSN-DS, and Edge-IIoTset, and perform simulations with different deep learning models across formulated unreliable network scenarios. The simulation results demonstrate ASR-Fed’s effectiveness in achieving optimal accuracy while significantly reducing communication costs when compared with other FL aggregating protocols.
Author	Ihekoronye, Vivian Ukamaka Nwakanma, Cosmas Ifeanyi Kim, Dong-Seong Lee, Jae Min
Author_xml	– sequence: 1 givenname: Vivian Ukamaka surname: Ihekoronye fullname: Ihekoronye, Vivian Ukamaka organization: IT-Convergence Engineering, Kumoh National Institute of Technology – sequence: 2 givenname: Cosmas Ifeanyi surname: Nwakanma fullname: Nwakanma, Cosmas Ifeanyi organization: IT-Convergence Engineering, Kumoh National Institute of Technology – sequence: 3 givenname: Dong-Seong orcidid: 0000-0002-2977-5964 surname: Kim fullname: Kim, Dong-Seong organization: IT-Convergence Engineering, Kumoh National Institute of Technology – sequence: 4 givenname: Jae Min surname: Lee fullname: Lee, Jae Min email: ljmpaul@kumoh.ac.kr organization: IT-Convergence Engineering, Kumoh National Institute of Technology
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Cites_doi	10.1016/j.cose.2021.102344 10.1155/2021/9361348 10.1109/LCOMM.2022.3140273 10.3390/drones6020046 10.1109/JSAC.2021.3118435 10.1109/ACCESS.2023.3247512 10.1155/2021/5844728 10.1109/JIOT.2022.3200121 10.1109/JSYST.2023.3236995 10.1016/j.iot.2022.100657 10.1109/TPDS.2023.3237752 10.1155/2016/4731953 10.1109/JSAIT.2022.3205475 10.3390/drones6110342 10.3390/app10082864 10.1109/MSP.2020.2975749 10.1109/TNNLS.2019.2953131 10.1109/TVT.2022.3220809 10.1109/TPAMI.2022.3196503 10.1109/TC.2020.2994391 10.1109/ICTC55196.2022.9952400 10.48550/arXiv.2007.14390 10.1109/CSCWD54268.2022.9776061 10.1109/IPDPS53621.2022.00100 10.1109/SECONWorkshops56311.2022.9926402 10.21227/mbc1-1h68 10.1109/ICPADS51040.2020.00030 10.1109/ICCCI50826.2021.9457024 10.1145/3560905.3568538 10.1109/MILCOM55135.2022.10017532 10.1109/SYNCHROINFO51390.2021.9488416
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Clients Communication Complex Systems Computational Intelligence Control Cybersecurity Deep learning Design Drones Effectiveness Efficiency Engineering Federated learning Flexibility Learning Machine learning Mechatronics Optimization techniques Original Article Pattern Recognition Privacy Robotics Systems Biology Wireless networks
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Title	ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network
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