Abstract
As blockchain technology continues to advance, it has become increasingly utilized as a fundamental infrastructure in various industries, such as business, justice, and finance. The widespread adoption of blockchain technology has created a pressing need for effective information exchange among different institutional units within blockchain networks. Fortunately, cross-chain technology has emerged as a promising solution for enhancing information interaction among diverse blockchain units. In this study, we examined several variables and employed multiple methodologies to validate our proposed hypothesis. Using cross-chain technology, we introduce a blockchain cross-chain federated learning framework (BCFL) that facilitates the interaction and mutual verification of data and parameters across different blockchains. This approach enables federated learning without the need to collect or coordinate model weights on a central server, while also enhancing the security of the federated learning process through the consensus algorithm mechanism of blockchains. Finally, we conduct a comparative analysis of the effectiveness of BCFL compared to traditional machine learning and centralized federated learning.
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References
Borkowski, M., McDonald, D., Ritzer, C., Schulte, S.: Towards atomic cross-chain token transfers: State of the art and open questions within tast. Distributed Systems Group TU Wien (Technische Universit at Wien), Report 8 (2018)
Cao, L., Song, B.: Blockchain cross-chain protocol and platform research and development. In: 2021 International Conference on Electronics, Circuits and Information Engineering (ECIE), pp. 264–269. IEEE (2021)
Deshpande, A., Herlihy, M.: Privacy-preserving cross-chain atomic swaps. In: Bernhard, M., et al. (eds.) FC 2020. LNCS, vol. 12063, pp. 540–549. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-54455-3_38
Hameed, K., Barika, M., Garg, S., Amin, M.B., Kang, B.: A taxonomy study on securing blockchain-based industrial applications: an overview, application perspectives, requirements, attacks, countermeasures, and open issues. J. Ind. Inf. Integr. 26, 100312 (2022)
Kim, H., Park, J., Bennis, M., Kim, S.L.: On-device federated learning via blockchain and its latency analysis. arXiv preprint arXiv:1808.03949 (2018)
Konečnỳ, J., McMahan, H.B., Ramage, D., Richtárik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)
Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial IoT. IEEE Trans. Industr. Inf. 16(6), 4177–4186 (2019)
Lys, L.: Security and reliability of cross-chain exchanges. (Sécurité et fiabilité des échanges inter-blockchain). Ph.D. thesis, Sorbonne University, Paris, France (2022). https://tel.archives-ouvertes.fr/tel-03847642
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273–1282. PMLR (2017)
Ou, W., Huang, S., Zheng, J., Zhang, Q., Zeng, G., Han, W.: An overview on cross-chain: mechanism, platforms, challenges and advances. Comput. Networks 218, 109378 (2022)
Singh, A., Click, K., Parizi, R.M., Zhang, Q., Dehghantanha, A., Choo, K.K.R.: Sidechain technologies in blockchain networks: an examination and state-of-the-art review. J. Netw. Comput. Appl. 149, 102471 (2020)
Sun, Y., Yi, L., Duan, L., Wang, W.: A decentralized cross-chain service protocol based on notary schemes and hash-locking. In: 2022 IEEE International Conference on Services Computing (SCC), pp. 152–157. IEEE (2022)
WeBankFinTech: Wecross: Open source cross-blockchain protocol (2019). https://doi.org/github.com/WeBankFinTech/WeCross
Weng, J., Weng, J., Zhang, J., Li, M., Zhang, Y., Luo, W.: Deepchain: auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Trans. Dependable Secure Comput. 18(5), 2438–2455 (2019)
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Yuan, Z., Sun, F., Cheng, Y., Wang, X. (2024). Federated Classification for Multiple Blockchain Systems. In: Li, C., Li, Z., Shen, L., Wu, F., Gong, X. (eds) Advanced Parallel Processing Technologies. APPT 2023. Lecture Notes in Computer Science, vol 14103. Springer, Singapore. https://doi.org/10.1007/978-981-99-7872-4_12
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DOI: https://doi.org/10.1007/978-981-99-7872-4_12
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