BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT

Xu, Yajing; Lu, Zhihui; Gai, Keke; Duan, Qiang; Lin, Jinzhong; Wu, Jie; Choo, Kim‐Kwang Raymond

doi:10.1109/jiot.2021.3138693

Cited by 49 publications

(4 citation statements)

References 29 publications

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“…In addition, most existing work assumes that participants are honest; however, malicious clients may launch attacks on intermediate results or the final model. Blockchain [64][65][66] provides a reliable method to ensure mutual trust in a distributed environment. The introduction of blockchain, leveraging features such as consensus mechanisms, smart contracts, and incentive mechanisms, provides a more secure model training process for FL [67,68].…”

Section: Federated Decision Treementioning

confidence: 99%

Decision Tree-Based Federated Learning: A Survey

Wang,

Gai

2024

Blockchains

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Federated learning (FL) has garnered significant attention as a novel machine learning technique that enables collaborative training among multiple parties without exposing raw local data. In comparison to traditional neural networks or linear models, decision tree models offer higher simplicity and interpretability. The integration of FL technology with decision tree models holds immense potential for performance enhancement and privacy improvement. One current challenge is to identify methods for training and prediction of decision tree models in the FL environment. This survey addresses this issue and examines recent efforts to integrate federated learning and decision tree technologies. We review research outcomes achieved in federated decision trees and emphasize that data security and communication efficiency are crucial focal points for FL. The survey discusses key findings related to data privacy and security issues, as well as communication efficiency problems in federated decision tree models. The primary research outcomes of this paper aim to provide theoretical support for the engineering of federated learning with decision trees as the underlying training model.

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Section: Federated Decision Treementioning

confidence: 99%

Decision Tree-Based Federated Learning: A Survey

Wang,

Gai

2024

Blockchains

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“…Journal [108] 2022 P34 [109] 2022 P35 [110] 2023 P36 [111] 2020 P37 [112] 2023 P38 [113] 2022 P39 [114] 2023 P40…”

Section: B Conducting the Reviewunclassified

Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review

Solis,

Marcelo Parra-Ullauri,

Kertesz

2024

IEEE Access

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The proliferation of Internet of Things (IoT) applications poses formidable challenges in managing data processing, privacy, and security. In response, technologies such as Fog Computing (FC), Blockchain (BC), and Federated Learning (FL) have emerged as promising solutions. Combining these technologies can broaden their scope, and impose novel challenges. This paper conducts a Systematic Literature Review (SLR) to investigate their integration within the IoT domain, systematically evaluating the current state-of-the-art by analyzing 40 papers against 38 extraction criteria, encompassing technical characteristics specific to FC, BC, FL, or their integration. The findings offer insights into the advantages, challenges, opportunities, and limitations of this integration, addressing data processing, privacy, and security concerns in IoT. By filling a research gap and directly examining FC, BC, and FL interoperability across architectural layers, this study contributes to knowledge expansion in the field. This paper proposes a novel framework for implementing FL and BC within FC environments for IoT applications, alongside a comprehensive synthesis of existing literature, distinguishing it from previous research efforts. Furthermore, it offers valuable insights into the current landscape, identifies research needs, and proposes future research directions. The framework and literature synthesis provided allow readers to access customized information on FC-BC-FL integration, aiding in designing and implementing robust IoT solutions.

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“…However, the trustworthiness of client devices is not adequately considered, leading to issues of abnormal devices affecting the accuracy of the global model. In another work, presented in reference [21], security and efficiency issues related to node participation in FL are addressed by proposing a blockchain-based secure incentive model. The detection of malicious nodes in this model employs a reputation-based node selection mechanism, which enhances security compared to the aforementioned approaches.…”

Section: Related Workmentioning

confidence: 99%

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

Luo,

Gong,

Zhu

et al. 2023

Applied Sciences

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The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.

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BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT

Cited by 49 publications

References 29 publications

Decision Tree-Based Federated Learning: A Survey

Decision Tree-Based Federated Learning: A Survey

Exploring the Synergy of Fog Computing, Blockchain, and Federated Learning for IoT Applications: A Systematic Literature Review

A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

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