Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence

By Meng Hao, Hongwei Li, Xizhao Luo, Guowen Xu, Haomiao Yang, Sen Liu. IEEE Transactions on Industrial Informatics, 2020

418 citations recorded by the source corpus.

By leveraging deep learning-based technologies, industrial artificial intelligence (IAI) has been applied to solve various industrial challenging problems in Industry 4.0. However, for privacy reasons, traditional centralized training may be unsuitable for sensitive data-driven industrial scenarios, such as healthcare and autopilot. Recently, federated learning has received widespread attention, since it enables participants to collaboratively learn a shared model without revealing their local data. However, studies have shown that, by exploiting the shared parameters adversaries can still compromise industrial applications such as auto-driving navigation systems, medical data in wearable devices, and industrial robots’ decision making. In this article, to solve this problem, we propose an efficient and privacy-enhanced federated learning (PEFL) scheme for IAI. Compared with existing solutions, PEFL is noninteractive, and can prevent private data from being leaked even if multiple entities collude with each other. Moreover, extensive experiments with real-world data demonstrate the superiority of PEFL in terms of accuracy and efficiency.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/a9bd625a66c8cb1cf6841faaa82862d55debf98f

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Citations in the corpus

Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System (2019, background): “[23] proposed a privacy-enhanced FL scheme to solve the privacy issue in FL.”
Federated Learning for Healthcare: Systematic Review and Architecture Proposal (2022, methodology): “Such an approach allows data processing while both models and data remain encrypted on FL. Hao et al. [82] employ homomorphic encryption and augmented learning with error to protect the parameters of local learning models during training.”
Algorithm fairness in artificial intelligence for medicine and healthcare (2023, background): “Federated learning is a distributed-learning paradigm in which a network of participants uses their own computing resources and local data to collectively train a global model stored on a server 202–205 .”
Local Differential Privacy-Based Federated Learning for Internet of Things (2020, background): “[31] propose a differential enhanced FL scheme for industrial artificial industry.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “As an example, a privacypreserved FL framework is introduced in [169] to realize”
A review of applications in federated learning (2020, background): “By this way, Hao et al., (2019) envisioned a more efficient”
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2019, background): “[32] proposed a privacy-enhanced FL scheme to solve the privacy issue in FL.”
Trustworthy Artificial Intelligence: A Review (2022, background): “3 Federated Learning [76, 85, 133, 194] 3 Machine Unlearning [22, 35]”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “• In the FL, the intermediate weights or gradients communicated between participants and the aggregator may reveal sensitive information about the participants’ training datasets, as shown in References [96, 137, 198, 212].”
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook (2020, background): “large volumes of data for centralized processing, which poses severe privacy concerns [95].”
Privacy‐preserving federated learning based on multi‐key homomorphic encryption (2021, background): “Hao et al.(16) proposed an efficient and privacy‐enhanced federated learning (PEFL) scheme.”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, neutral): “[73] propose a privacy-enhanced FL scheme for”