Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing

By Youyang Qu, Longxiang Gao, Tom H. Luan, Yong Xiang, Shui Yu, Bai Li, Gavin Zheng. IEEE Internet of Things Journal, 2020

386 citations recorded by the source corpus.

As the extension of cloud computing and a foundation of IoT, fog computing is experiencing fast prosperity because of its potential to mitigate some troublesome issues, such as network congestion, latency, and local autonomy. However, privacy issues and the subsequent inefficiency are dragging down the performances of fog computing. The majority of existing works hardly consider a reasonable balance between them while suffering from poisoning attacks. To address the aforementioned issues, we propose a novel blockchain-enabled federated learning (FL-Block) scheme to close the gap. FL-Block allows local learning updates of end devices exchanges with a blockchain-based global learning model, which is verified by miners. Built upon this, FL-Block enables the autonomous machine learning without any centralized authority to maintain the global model and coordinates by using a Proof-of-Work consensus mechanism of the blockchain. Furthermore, we analyze the latency performance of FL-Block and further derive the optimal block generation rate by taking communication, consensus delays, and computation cost into consideration. Extensive evaluation results show the superior performances of FL-Block from the aspects of privacy protection, efficiency, and resistance to the poisoning attack.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/d2da19b2ae5af19df02c27862f403883a71c82f5

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Citations in the corpus

Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “Therefore, there will be an additional step of model distribution and model aggregation in the fog layers, which can relieve the communication pressure between the cloud layer and the IoT layers [174].”
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging (2020, background): “[27], proposed a blockchain-based algorithm for the aggregation of the local deep learning models which ensure data reliability.”
The Roadmap to 6G Security and Privacy (2021, background): “Specially, in the big data era of decentralized systems, adding privacy protection mechanisms will further increase the communication and computational costs which already show a rapid growth [151].”
Decentralized Federated Learning: A Survey and Perspective (2023, methodology): “Kim et al. [110] and Qu et al. [111] have explored using blockchain technology to mitigate SPoF by replacing the central server role, but this approach diverges from the conventional CFL model.”
A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “Therefore, they (weight update, gradient update and the final model) contain sensitive information of local data [133, 142, 167].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “• In FLchain, attacks can make attempt to train a lo-cal model using designed falsiﬁed data to replace the global model and modify parameter values during model transmission, aiming to manipulate the training output.”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, methodology): “[68] use a blockchain-based approach to achieve decentralized”
A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, neutral): “Furthermore, some prior works [202]–[205] proposed mechanisms to preserve privacy in a blockchain-enabled FL-based IoT environment.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “To address the adversarial attacks, the blockchain based decentralized learning was proposed in [252] to enable secure global model aggregation by using a consensus mechanism of blockchain.”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “In this sense, time, cycles, and temperature are considered [87].”