ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ihekoronye, Vivian Ukamaka | - |
dc.contributor.author | Nwakanma, Cosmas Ifeanyi | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.contributor.author | Lee, Jae Min | - |
dc.date.accessioned | 2024-08-09T02:00:20Z | - |
dc.date.available | 2024-08-09T02:00:20Z | - |
dc.date.issued | 2024-07 | - |
dc.identifier.issn | 1868-8071 | - |
dc.identifier.issn | 1868-808X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28843 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.title | ASR-Fed: agnostic straggler-resilient semi-asynchronous federated learning technique for secured drone network | - |
dc.type | Article | - |
dc.publisher.location | 독일 | - |
dc.identifier.doi | 10.1007/s13042-024-02238-9 | - |
dc.identifier.scopusid | 2-s2.0-85198541485 | - |
dc.identifier.wosid | 001271168400001 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | - |
dc.citation.title | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | - |
dc.type.docType | Article; Early Access | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | CLIENT SELECTION | - |
dc.subject.keywordAuthor | Cybersecurity | - |
dc.subject.keywordAuthor | Drone security networks | - |
dc.subject.keywordAuthor | Federated learning | - |
dc.subject.keywordAuthor | Intrusion detection | - |
dc.subject.keywordAuthor | Semi-asynchronous technique | - |
dc.subject.keywordAuthor | Straggler effect | - |
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