CoPiFL: A collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption

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Xiong, Ruoting, Ren, Wei, Zhao, Shenghui, He, Jie, Ren, Yi, Choo, Kim-Kwang Raymond and Min, Geyong (2024) CoPiFL: A collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption. Future Generation Computer Systems, 156. pp. 95-104. ISSN 0167-739X

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Abstract

Federated learning (FL) is one of many tasks facilitated by crowdsourcing. Generally in such a setting, participating workers cooperate to train a comprehensive model by exchanging the trained parameters. While blockchain-based crowdsourcing approaches offer advantages such as data integrity and tamper-proof properties, platform designers must also address potential risks such as data leakage, de-anonymization, and collusion attacks. In this paper, we propose a collusion-resistant and privacy-protected FL crowdsourcing scheme implemented by smart contracts. Our proposed scheme supports fair reward distribution in FL, as well as ensuring data privacy and user privacy using homomorphic encryption and pseudo-identity techniques. The evaluation results show that the time cost and gas consumption of the proposed scheme are realistic in practice. Furthermore, we conduct a comparative evaluation of single worker and multi-workers (crowdsourcing) approaches using Alexnet, Resnet and VGG models, showing that FL with greedy mechanism can significantly accelerate the model training without compromising models’ accuracy. Finally, we present a comprehensive security analysis of the proposed approach.

Item Type:	Article
Additional Information:	Data availability: No data was used for the research described in the article. Acknowledgments: The research was financially supported by the open Foundation of Anhui Engineering Research Center of Intelligent Perception and Elderly Care, Chuzhou University (No. 2022OPA01), Guangxi Key Laboratory of Machine Vision and Intelligent Control (No. 2022B11), the Knowledge Innovation Program of Wuhan - Basic Research (No. 2022010801010197), the Opening Project of Nanchang Innovation Institute, Peking University (No. NCII2022A02), and National Natural Science Foundation of China (61961036, 62162054). The work of K.-K. R. Choo was supported only by the Cloud Technology Endowed Professorship .
Faculty \ School:	Faculty of Science Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Intelligence and Networks Faculty of Science > Research Groups > Data Science and AI
Related URLs:	https://linkinghub.elsevier.com/retrieve...
Depositing User:	LivePure Connector
Date Deposited:	19 May 2025 09:32
Last Modified:	19 May 2025 09:32
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99288
DOI:	10.1016/j.future.2024.03.016

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