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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Vydáno v:	International journal of machine learning and cybernetics Ročník 15; číslo 11; s. 5303 - 5319
Hlavní autoři:	Ihekoronye, Vivian Ukamaka, Nwakanma, Cosmas Ifeanyi, Kim, Dong-Seong, Lee, Jae Min
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer Nature B.V
Témata:	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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1868-8071 1868-808X
DOI:	10.1007/s13042-024-02238-9