A hierarchical blockchain-enabled distributed federated learning system with model contribution based rewarding

Abstract

Distributed Federated Learning (DFL) technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets, making it a desirable solution for decentralized and privacy-preserving Web3 scenarios. However, DFL faces incentive and security challenges in the decentralized framework. To address these issues, this paper presents a Hierarchical Blockchain-enabled DFL (HBDFL) system, which provides a generic solution framework for the DFL-related applications. The proposed system consists of four major components, including a model contribution-based reward mechanism, a Proof of Elapsed Time and Accuracy (PoETA) consensus algorithm, a Distributed Reputation-based Verification Mechanism (DRTM) and an Accuracy-Dependent Throughput Management (ADTM) mechanism. The model contribution-based rewarding mechanism incentivizes network nodes to train models with their local datasets, while the PoETA consensus algorithm optimizes the tradeoff between the shared model accuracy and system throughput. The DRTM improves the system efficiency in consensus, and the ADTM mechanism guarantees that the throughput performance remains within a predefined range while improving the shared model accuracy. The performance of the proposed HBDFL system is evaluated by numerical simulations, with the results showing that the system improves the accuracy of the shared model while maintaining high throughput and ensuring security.

Keywords

Blockchain / Federated learning / Consensus scheme / Accuracy dependent throughput management

CRediT authorship contribution statement

Haibo Wang: Writing - review & editing, Funding acquisition, Conceptualization. Hongwei Gao: Writing - original draft, Validation, Software, Formal analysis, Data curation. Teng Ma: Writing - original draft, Methodology, Investigation, Conceptualization. Chong Li: Supervision, Methodology. Tao Jing: Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] A. Hard, K. Rao, R. Mathews, Federated learning for mobile keyboard prediction, preprint, arXiv :1811.03604, 2018.

[2] Y. Wang, Z. Su, J. Ni, N. Zhang, X. Shen, Blockchain-empowered space-air-ground intergrated networks: opportunities, challenges, and solutions, IEEE Commun. Surv. Tutor. 24 (1) (2022) 160-209.

[3] M. Nofer, P. Gomber, O. Hinz, Blockchain, Bus. Inf. Syst. Eng. 59 (3) (2017) 183-187.

[4] M. Scherer, Performance and scalability of blockchain networks and smart contracts, Dissertation, 2017.

[5] M.S. Ali, M. Vecchio, M. Pincheira, Applications of blockchains in the Internet of things: a comprehensive survey, IEEE Commun. Surv. Tutor. 21 (2) (2018) 1676-1717.

[6] I. Martinez, S. Francis, A.S. Hafid, Record and reward federated learning contribu-tions with blockchain, in: International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), IEEE, 2019, pp. 50-57.

[7] R. Zhang, B. Preneel, Lay down the common metrics: evaluating proof-of-work con-sensus protocols’ security, in: 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2019, pp. 175-192.

[8] Y. Zhou, L. Liu, L. Wang, N. Hui, X. Cui, J. Wu, Y. Peng, Y. Qi, C. Xing,Service-aware 6G: an intelligent and open network based on the convergence of communication, computing and caching, Digital Communications and Networks 6 (2020) 253-260.

[9] Y. Lu, X. Huang, Y. Dai, Blockchain and federated learning for privacy-preserved data sharing in industrial IoT, IEEE Trans. Ind. Inform. 16 (6) (2019) 4177-4186.

[10] J. Li, Y. Shao, K. Wei, Blockchain assisted decentralized federated learning (BLADE-FL): performance analysis and resource allocation, preprint, arXiv :2101.06905, 2021.

[11] R. Kumar, A.A. Khan, J. Kumar, Blockchain-federated-learning and deep learn-ing models for covid-19 detection using ct imaging, IEEE Sens. J. 21 (14) (2021) 16301-16314.

[12] S.R. Pokhrel, J. Choi, Federated learning with blockchain for autonomous vehicles: analysis and design challenges, IEEE Trans. Commun. 68 (8) (2020) 4734-4746.

[13] S. Singh, S. Rathore, O. Alfarraj, A. Tolba, B. Yoon, A framework for privacy-preservation of IoT healthcare data using federated learning and blockchain tech-nology, Future Gener. Comput. Syst. 129 (2022) 380-388.

[14] Y. Qu, L. Gao, T. Luan, Y. Xiang, S. Yu, B. Li, G. Zheng, Decentralized privacy using blockchain-enabled federated learning in fog computing, IEEE Int. Things J. 7 (6) (2020) 5171-5183.

[15] Jun Liu, Mingyue Xie, Shuyu Chen, Chuang Ma, Qianhong Gong, An improved DPoS consensus mechanism in blockchain based on PLTS for the smart autonomous multi-robot system, Inf. Sci. 575 (2021) 528-541.

[16] Y. Lu, X. Huang, K. Zhang, Blockchain empowered asynchronous federated learning for secure data sharing in Internet of vehicles, IEEE Trans. Veh. Technol. 69 (4) (2020) 4298-4311.

[17] Y. Li, C. Chen, N. Liu, H. Huang, Z. Zheng, Q. Yan, A blockchain-based decentralized federated learning framework with committee consensus, IEEE Netw. 35 (1) (2021) 234-241.

[18] M.H. ur. Rehman, K. Salah, E. Damiani, Towards blockchain-based reputation-aware federated learning, in: IEEE INFOCOM 2020-IEEE Conference on Computer Commu-nications Workshops (INFOCOM WKSHPS), IEEE, 2020, pp. 183-188.

[19] C. Dwork, A. Roth, The algorithmic foundations of differential privacy, Found. Trends Theor. Comput. Sci. 9 (3-4) (2014) 211-407.

[20] S. Muralidharan, H. Ko, An inter planetary file system (IPFS) based IoT frame-work, in: IEEE International Conference on Consumer Electronics (ICCE), IEEE, 2019, pp. 1-2.

[21] B. McMahan, E. Moore, D. Ramage, S. Hampson, B. Arcas,Communication-efficient learning of deep networks from decentralized data, in:Proc. 20th Int. Conf. Artif. Intell. Statist, 2017, pp. 1273-1282.

[22] L. Chen, L. Xu, N. Shah, On security analysis of proof-of-elapsed-time (poet),in:In-ternational Symposium on Stabilization, Safety, and Security of Distributed Systems, Springer, Cham, 2017, pp. 282-297.

[23] G. Cohen, S. Afshar, J. Tapson, EMNIST: Extending MNIST to handwritten letters, in: IEEE International Joint Conference on Neural Networks (IJCNN), IEEE, 2017, pp. 2921-2926.

[24] Shivam S. Kadam, Amol C. Adamuthe, Ashwini B. Patil, CNN model for image clas-sification on MNIST and fashion-MNIST dataset, J. Sci. Res. (2020) 374-384.