Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

Hongmin Gao; Xiaofeng Pan; Xiaojing Zhang; Keke Ye; Peng Zhang; Long Sun

doi:10.1117/12.3009373

11 October 2023 Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

Hongmin Gao, Xiaofeng Pan, Xiaojing Zhang, Keke Ye, Peng Zhang, Long Sun

Author Affiliations +

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 1291823 (2023) https://doi.org/10.1117/12.3009373
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

The 14th Five-Year Plan proposes the objective of expediting digital development and constructing a digital China, aiming to minimize the reliance on physical travel while fostering increased data flow and sharing. However, the implementation of data privacy regulations, such as the Personal Information Protection Law, has resulted in conflicts between data sharing and privacy. To address this challenge, federated learning technology has emerged as an effective solution that enables data sharing while safeguarding privacy. Nonetheless, trust issues arise among federated members in relation to each other and central servers. This is where blockchain technology, with its notable features of verifiability, auditability, and traceability, can play a pivotal role in resolving trust concerns within federated learning. Consequently, a novel framework called trusted federated learning based on blockchain has emerged. This article initially defines the trust issue in federated learning and subsequently provides a chronological overview of research advancements in the domain of trusted federated learning based on blockchain. Furthermore, it discusses the advantages and disadvantages of existing research across three categories: blockchain structure design, consensus mechanism, and smart contracts. Additionally, the article highlights the application characteristics of this framework in fields such as the Internet of Things, connected vehicles, and medical services. Finally, the core challenges and future research directions of trusted federated learning based on blockchain are outlined.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongmin Gao, Xiaofeng Pan, Xiaojing Zhang, Keke Ye, Peng Zhang, and Long Sun "Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 1291823 (11 October 2023); https://doi.org/10.1117/12.3009373

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KEYWORDS

Machine learning

Blockchain

Data modeling

Education and training

Data privacy

Internet of things

Biomedical applications

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