TrustFed: A Framework for Fair and Trustworthy Cross-Device Federated Learning in IIoT
Open accessAbstract
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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In a real-world healthcare system, data gathered from a single medical lab is not enough, and an ML model based on that dataset will not give promising results.
“In a real-world healthcare system, the data gathered from a single medical lab is not enough, and the ML model based on that dataset will not give promising results [47].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [47]
TrustFed uses Industrial IoT devices as federated learning candidates and smart contract technology over the Ethereum blockchain to maintain participants' reputations.
“For example, [76] presents TrustFed, which uses Industrial IoT devices as federated learning candidates and smart contract technology over the Ethereum blockchain to maintain participants' reputations.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [76]
TrustFed can identify and eliminate outliers in the training distributions before combining the model updates.
“The solution can also identify and eliminate outliers in the training distributions before combining the model updates.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [76]
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References 19 · Cited by 132
Research works printed but not linked: 1
- P. Kairouz et al., "Advances and open problems in federated learning," 2019, arXiv:1912.04977. · arXiv
- Authors
- Rehman, M. H. U., Dirir, A. M., Salah, K., Damiani, E., & Svetinovic, D.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-12
- Volume
- 17
- Issue
- 12
- Pages
- 8485-8494
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3159295158
- Cited by
- 132
- References
- 19
- Citations by year
- 2026: 13 · 2025: 30 · 2024: 29 · 2023: 36 · 2022: 18 · 2021: 6
- FWCI
- 7.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion, cc-by)
- WU Research (submittedVersion, cc-by)
- Funders
- Cyber-Physical Systems, Khalifa University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- cross-device federated learning, Industrial Internet of Things, blockchain, smart contracts, model poisoning, attack detection, fairness, outlier detection
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