A Federated Learning Model for Privacy Protection Based on Blockchain and Dynamic Evaluation

Liu Wei; Tang Congke; Ma Jie; Tian Zhao; Wang Qi; She Wei

doi:10.7544/issn1000-1239.202330269

Journal of Computer Research and Development > 2023 > 60(11): 2583-2593. > DOI: 10.7544/issn1000-1239.202330269

Liu Wei, Tang Congke, Ma Jie, Tian Zhao, Wang Qi, She Wei. A Federated Learning Model for Privacy Protection Based on Blockchain and Dynamic Evaluation[J]. Journal of Computer Research and Development, 2023, 60(11): 2583-2593. DOI: 10.7544/issn1000-1239.202330269

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A Federated Learning Model for Privacy Protection Based on Blockchain and Dynamic Evaluation

Liu Wei^{1, 2, 3,},
Tang Congke^{1, 3},
Ma Jie^{1, 3},
Tian Zhao^{1, 3},
Wang Qi⁴,
She Wei^{1, 3, ,}

1.
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002
2.
Henan Key Laboratory of Network Cryptography Technology (Information Engineering University), Zhengzhou 450000
3.
Zhengzhou Key Laboratory of Blockchain and Data Intelligence (Zhengzhou University), Zhengzhou 450000
4.
Library of Zhengzhou University, Zhengzhou University, Zhengzhou 450001

Funds: This work was supported by the Program for Science＆Technology Innovation Talents in Universities of Henan Province (21HASTIT031), the Major Public Welfare Project of Henan Province (201300210300), the Scientific and Technological Research Project in Henan Province (212102310039), and the Project of Henan Key Laboratory of Network Cryptography Technology (LNCT2022-A04).

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Author Bio:
Liu Wei: born in 1981. PhD, associate professor, PhD supervisor. His main research interests include blockchain technology, privacy protection, and smart healthcare

Tang Congke: born in 1999. Master. His main research interests include blockchain technology and federated learning

Ma Jie: born in 1997. Master. His main research interests include blockchain technology and federated learning

Tian Zhao: born in 1985. PhD, associate professor, master supervisor. His main research interests include blockchain technology, information security, and intelligent transport

Wang Qi: born in 1986. PhD, master supervisor. His main research interests include blockchain technology, machine learning, and big data technology

She Wei: born in 1977. PhD, professor, PhD supervisor. His main research interests include blockchain technology, Internet of energy, and Internet healthcare
Received Date: January 19, 2023
Revised Date: June 04, 2023
Available Online: June 25, 2023

Graphical Abstract

Abstract

Abstract

While federated learning is widely used as a privacy-preserving technology, new challenges such as instability of the central server and privacy leakage caused by the interaction between the federated learning server and the participants as well as security issues have arisen. A privacy-preserving federated learning model based on blockchain and dynamic evaluation is proposed. Local training is performed using sparsification, and global model updates are protected using differential privacy to solve the privacy leakage problem in the federated learning process, using digital signature and double Hash comparisons to verify the identity of the participants and the ownership of the trained model after the completion of local training. In addition, multiple weight dynamic evaluation method is used to calculate the model’s single-round evaluation and the final evaluation of the participants. Experiments show that the proposed model can effectively solve the single-point failure and local model verification problems in federation learning, and the use of sparsification and differential privacy can guarantee the security of the model with a slight loss of accuracy compared with traditional federation learning. The evaluation of the local model and the participants are scored as the basis for the participants’ contributions, thus effectively ensuring the fairness of the incentive mechanism.
- federated learning,
- blockchain,
- sparsification,
- differential privacy,
- digital signature,
- dynamic evaluation

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References (28)

References

[1]	Mcmahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data[C] //Proc of the 20th Int Conf on Artificial Intelligence and Statistics. New York: PMLR, 2017: 1273−1282
[2]	Rodriguez-Barroso N, Jimenez-Lopez D, Luzon M V, et al. Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges[J]. Information Fusion, 2023, 90: 148−173 doi: 10.1016/j.inffus.2022.09.011
[3]	Zhu Ligeng, Liu Zhijian, Han Song. Deep leakage from gradients[J]. Advances in Neural Information Processing Systems, 2019, 32: 14774−14784
[4]	Zhao Bo, Mopuri K R, BILEN H. IDLG: Improved deep leakage from gradients[J]. arXiv preprint, arXiv: 2001.02610, 2020
[5]	Melis L, Song C, Cristofaro E De, et al. Exploiting unintended feature leakage in collaborative learning[C] //Proc of 2019 IEEE Symp on Security and Privacy (SP). Piscataway, NJ: IEEE, 2019: 691−706
[6]	Hitaj B, Ateniese G, Perez-Cruz F. Deep models under the GAN: Information leakage from collaborative deep learning[C] //Proc of the 2017 ACM SIGSAC Conf on Computer and Communications Security. New York: ACM, 2017: 603−618
[7]	Shafahi A, Huang W R, Najibi M, et al. Poison frogs! targeted clean-label poisoning attacks on neural networks[J]. Advances in Neural Information Processing Systems, 2018, 31: 6103−6113
[8]	Sun Gan, Yang Cong, Dong Jiahua, et al. Data poisoning attacks on federated machine learning[J]. IEEE Internet of Things Journal, 2021, 9(13): 11365−11375
[9]	Bhagoji A N, Chakraborty S, Mittal P, et al. Analyzing federated learning through an adversarial lens[C] //Proc of Int Conf on Machine Learning. New York: PMLR, 2019: 634−643
[10]	Shokri R, Stronati M, Song C, et al. Membership inference attacks against machine learning models[C] //Proc of 2017 IEEE Symp on Security and Privacy (SP). Piscataway, NJ: IEEE, 2017: 3−18
[11]	Tan Alysa Ziying, Yu Han, Cui Lizhen, et al. Towards personalized federated learning[J]. arXiv preprint, arXiv: 2103.00710, 2021
[12]	Nguyen D C, Ding Ming, Pham Q V, et al. Federated learning meets blockchain in edge computing: Opportunities and challenges[J]. IEEE Internet of Things Journal, 2021, 8(16): 12806−12825 doi: 10.1109/JIOT.2021.3072611
[13]	Gosselin R, Vieu L, Loukil F, et al. Privacy and security in federated learning: A survey[J]. Applied Sciences, 2022, 12(19): 9901
[14]	Qu Youyang, Uddin M P, Gan Chenquan, et al. Blockchain-enabled federated learning: A survey[J]. ACM Computing Surveys, 2022, 55(4): 1−35
[15]	Kim H, Park J, Bennis M, et al. Blockchained on-device federated learning[J]. IEEE Communications Letters, 2019, 24(6): 1279−1283
[16]	Liu Yuan, Sun Shuai, Ai Zhengpeng, et al. FedCoin: A peer-to-peer payment system for federated learning[M]//Federated Learning: Privacy and Incentive. Cham: Springer International Publishing, 2020: 125−138
[17]	Peng Zhe, Xu Jianliang, Chu Xiaowen, et al. VFChain: Enabling verifiable and auditable federated learning via blockchain systems[J]. IEEE Transactions on Network Science and Engineering, 2021, 9(1): 173−186
[18]	Li Yuzheng, Chen Chuan, Liu Nan, et al. A blockchain-based decentralized federated learning framework with committee consensus[J]. IEEE Network, 2020, 35(1): 234−241
[19]	高胜,袁丽萍,朱建明,等. 一种基于区块链的隐私保护异步联邦学习[J]. 中国科学:信息科学,2021,51(10):1755−1774 doi: 10.1360/SSI-2021-0087 Gao Sheng, Yuan Liping, Zhu Jianming, et al. A privacy-preserving asynchronous federated learning based on blockchain[J]. SCIENTIA SINICA Informationis, 2021, 51(10): 1755−1774 (in Chinese) doi: 10.1360/SSI-2021-0087
[20]	Wainakh A, Guinea A S, Grube T, et al. Enhancing privacy via hierarchical federated learning[C] //Proc of 2020 IEEE European Symp on Security and Privacy Workshops (EuroS&PW). Piscataway, NJ: IEEE, 2020: 344−347
[21]	Shayan M, Fung C, Yoon C J M, et al. Biscotti: A blockchain system for private and secure federated learning[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 32(7): 1513−1525
[22]	Zhao Yang, Zhao Jun, Jiang Linshan, et al. Privacy-preserving blockchain-based federated learning for IoT devices[J]. IEEE Internet of Things Journal, 2020, 8(3): 1817−1829
[23]	周炜,王超,徐剑,等. 基于区块链的隐私保护去中心化联邦学习模型[J]. 计算机研究与发展,2022,59(11):2423−2436 doi: 10.7544/issn1000-1239.20220470 Zhou Wei, Wang Chao, Xu Jian, et al. Privacy-preserving and decentralized federated learning model based on the blockchain[J]. Journal of Computer Research and Development, 2022, 59(11): 2423−2436 (in Chinese) doi: 10.7544/issn1000-1239.20220470
[24]	祝烈煌,高峰,沈蒙,等. 区块链隐私保护研究综述[J]. 计算机研究与发展,2017,54(10):2170−2186 doi: 10.7544/issn1000-1239.2017.20170471 Zhu Liehuang, Gao Feng, Shen Meng, et al. Survey on privacy preserving techniques for blockchain technology[J]. Journal of Computer Research and Development, 2017, 54(10): 2170−2186 (in Chinese) doi: 10.7544/issn1000-1239.2017.20170471
[25]	Eghlidi N F, Jaggi M. Sparse communication for training deep networks[J]. arXiv preprint, arXiv: 2009.09271, 2020
[26]	Dwork C, Roth A. The algorithmic foundations of differential privacy[J]. Foundations and Trends® in Theoretical Computer Science, 2014, 9(3–4): 211−407
[27]	Dwork C. Differential privacy: A survey of results[C] //Proc of Int Conf on Theory and Applications of Models of Computation. Berlin: Springer, 2008: 1−19
[28]	Ma Chuan, Li Jun, Shi Long, et al. When federated learning meets blockchain: A new distributed learning paradigm[J]. IEEE Computational Intelligence Magazine, 2022, 17(3): 26−33 doi: 10.1109/MCI.2022.3180932

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