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ART4FL: An Agent-based Architectural Approach for Trustworthy Federated Learning in the IoT
Malmö University.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-6309-2892
Birzeit University, Palestine.
Malmö University.
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2023 (English)In: 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023 / [ed] Quwaider M., Awaysheh F.M., Jararweh Y., Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 270-275Conference paper, Published paper (Refereed)
Abstract [en]

The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems’ users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents’ trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents’ during the federations’ formation phase. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 270-275
Keywords [en]
Agents, Internet of Things, Machine Learning, Trustworthy Federated Learning, Learning systems, Agent based, Architectural approach, Learning frameworks, Machine learning models, Machine learning technology, Machine-learning, Similar datum, Support systems, Trust scores
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-25824DOI: 10.1109/FMEC59375.2023.10306036ISI: 001103180200036Scopus ID: 2-s2.0-85179515213ISBN: 9798350316971 (print)OAI: oai:DiVA.org:bth-25824DiVA, id: diva2:1822952
Conference
2023 8th International Conference on Fog and Mobile Edge Computing, FMEC 2023, Tartu, 18/9 - 20/9 2023
Projects
Intelligent and Trustworthy IoT Systems
Funder
Knowledge Foundation, 20220087Available from: 2023-12-28 Created: 2023-12-28 Last updated: 2023-12-31Bibliographically approved

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fulltext(652 kB)168 downloads
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Alawadi, Sadi

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Citation style
  • apa
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  • de-DE
  • en-GB
  • en-US
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