Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

Abstract

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

Keywords

Blockchain / Signcryption / Federated learning / Asynchronous / Fog computing

CRediT authorship contribution statement

Zhou Zhou: Writing - original draft, Formal analysis, Methodology. Youliang Tian: Resources, Supervision, Writing - review & editing. Jinbo Xiong: Conceptualization, Supervision, Writing - review & editing. Changgen Peng: Project administration, Supervision. Jing Li: Validation. Nan Yang: Validation.

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.

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant No. 2021YFB3101100, in part by the National Natural Science Foundation of China under Grant 62272123, 62272102, 62272124, in part by the Project of High-level Innovative Talents of Guizhou Province under Grant [2020]6008, in part by the Science and Technology Program of Guizhou Province under Grant No. [2020]5017, No. [2022]065, in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS202105.

References

[1] C. Puliafito, E. Mingozzi, F. Longo, A. Puliafito, O. Rana, Fog computing for the Internet of things: a survey, ACM Trans. Internet Technol. 19 (2) (2019) 1-41.

[2] D. Wu, Z. Yang, B. Yang, R. Wang, P. Zhang, From centralized management to edge collaboration: a privacy-preserving task assignment framework for mobile crowd-sensing, IEEE Int. Things J. 8 (6) (2020) 4579-4589.

[3] M. Mukherjee, L. Shu, D. Wang, Survey of fog computing: fundamental, network applications, and research challenges, IEEE Commun. Surv. Tutor. 20 (3) (2018) 1826-1857.

[4] D. Wu, X. Han, Z. Yang, R. Wang, Exploiting transfer learning for emotion recog-nition under cloud-edge-client collaborations, IEEE J. Sel. Areas Commun. 39 (2) (2020) 479-490.

[5] A. Alrawais, A. Alhothaily, C. Hu, X. Cheng, Fog computing for the Internet of things: security and privacy issues, IEEE Internet Comput. 21 (2) (2017) 34-42.

[6] P. Hu, H. Ning, T. Qiu, H. Song, Y. Wang, X. Yao, Security and privacy preserva-tion scheme of face identification and resolution framework using fog computing in Internet of things, IEEE Int. Things J. 4 (5) (2017) 1143-1155.

[7] H.B. McMahan, E. Moore, D. Ramage, B.A. y Arcas,Federated learning of deep networks using model averaging, arXiv preprint, arXiv :1602.05629, 2016.

[8] Z. Zhou, Y. Tian, J. Xiong, J. Ma, C. Peng, Blockchain-enabled secure and trusted federated data sharing in IIoT, IEEE Trans. Ind. Inform. 19 (5) (2023) 6669-6681.

[9] C. Zhou, A. Fu, S. Yu, W. Yang, H. Wang, Y. Zhang, Privacy-preserving federated learning in fog computing, IEEE Int. Things J. 7 (11) (2020) 10782-10793.

[10] Z. Zhao, C. Feng, H.H. Yang, X. Luo, Federated-learning-enabled intelligent fog radio access networks: fundamental theory, key techniques, and future trends, IEEE Wirel. Commun. 27 (2) (2020) 22-28.

[11] R. Saha, S. Misra, P.K. Deb, Fogfl: fog-assisted federated learning for resource-constrained iot devices, IEEE Int. Things J. 8 (10) (2020) 8456-8463.

[12] Y. Liu, Y. Dong, H. Wang, H. Jiang, Q. Xu, Distributed fog computing and federated learning enabled secure aggregation for iot devices, IEEE Int. Things J. 9 (21) (2022) 21025-21037.

[13] Z. Yu, J. Hu, G. Min, Z. Wang, W. Miao, S. Li, Privacy-preserving federated deep learning for cooperative hierarchical caching in fog computing, IEEE Int. Things J. 9 (22) (2021) 22246-22255.

[14] Z. Yang, X. Zhang, D. Wu, R. Wang, P. Zhang, Y. Wu, Efficient asynchronous feder-ated learning research in the Internet of vehicles, IEEE Int. Things J. 10 (9) (2022) 7737-7748.

[15] J. Kang, Z. Xiong, D. Niyato, S. Xie, J. Zhang, Incentive mechanism for reliable fed-erated learning: a joint optimization approach to combining reputation and contract theory, IEEE Int. Things J. 6 (6) (2019) 10700-10714.

[16] Y. Li, H. Li, G. Xu, T. Xiang, R. Lu, Practical privacy-preserving federated learning in vehicular fog computing, IEEE Trans. Veh. Technol. 71 (5) (2022) 4692-4705.

[17] Y. Qu, L. Gao, T.H. 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.

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

[19] Z. Peng, J. Xu, X. Chu, S. Gao, Y. Yao, R. Gu, Y. Tang, Vfchain: enabling verifiable and auditable federated learning via blockchain systems, IEEE Trans. Netw. Sci. Eng. 9 (1) (2021) 12311-12322.

[20] Y. He, K. Huang, G. Zhang, F.R. Yu, J. Chen, J. Li, Bift: a blockchain-based federated learning system for connected and autonomous vehicles, IEEE Int. Things J. 9 (14) (2021) 173-186.

[21] J. Xiong, J. Ren, L. Chen, Z. Yao, M. Lin, D. Wu, B. Niu, Enhancing privacy and availability for data clustering in intelligent electrical service of iot, IEEE Int. Things J. 6 (2) (2018) 1530-1540.

[22] Y. Liu, K. Wang, Y. Lin, W. Xu, Lightchain: a lightweight blockchain system for industrial Internet of things, IEEE Trans. Ind. Inform. 15 (6) (2019) 3571-3581.

[23] J. Shi, B. Ge, Y. Liu, Y. Yan, S. Li, Data privacy security guaranteed network intru-sion detection system based on federated learning, in: IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2021, pp. 1-6.

[24] M. Abadi, A. Chu, I. Goodfellow, H.B. McMahan, I. Mironov, K. Talwar, L. Zhang, Deep learning with differential privacy, in: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS), ACM, 2016, pp. 308-318.

[25] R. Bi, M. Zhao, Z. Ying, Y. Tian, J. Xiong, Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of iot, Digit. Commun. Netw. 10 (2) (2022) 380-388.

[26] K. Wei, J. Li, M. Ding, C. Ma, H.H. Yang, F. Farokhi, S. Jin, T.Q. Quek, H.V. Poor, Federated learning with differential privacy: algorithms and performance analysis, IEEE Trans. Inf. Forensics Secur. 15 (2020) 3454-3469.

[27] G. Xu, Y. Liu, P.W. Khan, Improvement of the dpos consensus mechanism in blockchain based on vague sets, IEEE Trans. Ind. Inform. 16 (6) (2019) 4252-4259.

[28] S.S. Al-Riyami, K.G. Paterson, Certificateless public key cryptography, in: Interna-tional Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT), Springer, 2003, pp. 452-473.

[29] K.M. Tjørve, E. Tjørve, The use of Gompertz models in growth analyses, and new Gompertz-model approach: an addition to the unified-Richards family, PLoS ONE 12 (6) (2017) e0178691.

[30] P.-T. De Boer, D.P. Kroese, S. Mannor, R.Y. Rubinstein, A tutorial on the cross-entropy method, Ann. Oper. Res. 134 (2005) 19-67.

[31] A. Karati, S.H. Islam, G. Biswas, M.Z.A. Bhuiyan, P. Vijayakumar, M. Karuppiah, Provably secure identity-based signcryption scheme for crowdsourced industrial In-ternet of things environments, IEEE Int. Things J. 5 (4) (2017) 2904-2914.