Feraudo, Angelo, Yadav, Poonam orcid.org/0000-0003-0169-0704, Safronov, Vadim et al. (5 more authors) (2020) CoLearn:Enabling federated learning in MUD-compliant IoT edge networks. In: EdgeSys 2020 - Proceedings of the 3rd ACM International Workshop on Edge Systems, Analytics and Networking, Part of EuroSys 2020. 3rd ACM International Workshop on Edge Systems, Analytics and Networking, in conjunction with ACM EuroSys 2020, 27 Apr 2020 EdgeSys 2020 - Proceedings of the 3rd ACM International Workshop on Edge Systems, Analytics and Networking, Part of EuroSys 2020 . Association for Computing Machinery, Inc , GRC , 25–30.
Abstract
Edge computing and Federated Learning (FL) can work in tandem to address issues related to privacy and collaborative distributed learning in untrusted IoT environments. However, deployment of FL in resource-constrained IoT devices faces challenges including asynchronous participation of such devices in training, and the need to prevent malicious devices from participating. To address these challenges we present CoLearn, which build on the open-source Manufacturer Usage Description (MUD) implementation osMUD and the FL framework PySyft. We deploy CoLearn on resource-constrained devices in a lab environment to demonstrate (i) an asynchronous participation mechanism for IoT devices in machine learning model training using a publish/subscribe architecture, (ii) a mechanism for reducing the attack surface in FL architecture by allowing only IoT MUD-compliant devices to participate in the training phases, and (iii) a trade-off between communication bandwidth usage, training time and device temperature (thermal fatigue).
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Keywords: | Anomaly detection,Distributed machine learning,Edge computing,Federated learning,Internet of things (IoT),Machine learning,Manufacturer usage description,Traffic filtering |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 28 Oct 2020 16:00 |
Last Modified: | 02 Apr 2025 23:33 |
Published Version: | https://doi.org/10.1145/3378679.3394528 |
Status: | Published |
Publisher: | Association for Computing Machinery, Inc |
Series Name: | EdgeSys 2020 - Proceedings of the 3rd ACM International Workshop on Edge Systems, Analytics and Networking, Part of EuroSys 2020 |
Identification Number: | 10.1145/3378679.3394528 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167033 |