Rehman, Muhammad Habib Ur, Dirir, Ahmed Mukhtar, Salah, Khaled, Damiani, Ernesto, Svetinovic, Davor. 2021. TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT. IEEE Transactions on Industrial Informatics. 17 8485-8494.
BibTeX
Abstract
Cross-device federated learning (CDFL) systems enable fully decentralized training networks whereby each participating device can act as a model-owner and a model-producer. CDFL systems need to ensure fairness, trustworthiness, and high-quality model availability across all the participants in the underlying training networks. This article presents a blockchain-based framework, TrustFed, for CDFL systems to detect the model poisoning attacks, enable fair training settings, and maintain the participating devices' reputation. TrustFed provides fairness by detecting and removing the attackers from the training distributions. It uses blockchain smart contracts to maintain participating devices' reputations to compel the participants in bringing active and honest model contributions. We implemented the TrustFed using a Python-simulated federated learning framework, blockchain smart contracts, and statistical outlier detection techniques. We tested it over the large-scale industrial Internet of things dataset and multiple attack models. We found that TrustFed produces better results regarding multiple aspects compared with the conventional baseline approaches.
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Status of publication | Published |
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Affiliation | External |
Type of publication | Journal article |
Journal | IEEE Transactions on Industrial Informatics |
Citation Index | SCI |
Language | English |
Title | TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT |
Volume | 17 |
Year | 2021 |
Page from | 8485 |
Page to | 8494 |
Reviewed? | Y |
URL | http://xplorestaging.ieee.org/ielx7/9424/9523447/09416805.pdf?arnumber=9416805 |
DOI | http://dx.doi.org/10.1109/tii.2021.3075706 |
Open Access | Y |
Open Access Link | https://ieeexplore.ieee.org/document/9416805 |
Associations
- People
- Svetinovic, Davor (Details)
- External
- Damiani, Ernesto (Khalifa University | KU · Artificial Intelligence and Intelligent Systems Institute, United Arab Emirates)
- Dirir, Ahmed Mukhtar (Khalifa University of Science and Technology, United Arab Emirates)
- Rehman, Muhammad Habib Ur (Khalifa University of Science and Technology, United Arab Emirates)
- Salah, Khaled (Khalifa University | KU · Department of Electrical and Computer Engineering, United Arab Emirates)
- Organization
- Information Systems and Operations Management DP (Details)