Abstract
Existing supply chain information security methods all suffer from the difficulty of balancing information sharing efficiency and information privacy protection. Data is prone to leakageresulting in an increase in the overall risk of the supply chain. In response to this situationthe study proposes a supply chain information security sharing method based on a blockchain consensus algorithm and federated learning. The study designs a blockchain formula algorithm based on a verifiable mechanism and combines this algorithm with federated learning to construct an encryption model. To address the issue of privacy leakage that is prone to occur in federated learningthis study introduces casual pseudo-random functions and cuckoo hashing to process data and reduce communication complexitythereby avoiding hash conflicts. Finallythe encryption model is applied to the data transmission systemand combined with multi-factor authenticationthe secure sharing of supply chain information is achieved. The experiment results indicated that the average latency of the consensus algorithm during node election was 115.20msand during node replacementthe average latency was 8.56ms. The information security sharing method proposed in the study achieved an accuracy rate of 92.48% in generating data after processingwith a data tampering detection rate of 98.87%. The frequency of privacy breaches during the experiment was only 0.1%and the average response time was 1.0 s. The study proposes a new technical framework that takes into account both privacy protection and efficient sharingeffectively balancing the trust establishment and data security requirements in multi-party collaboration in the supply chainand providing a verifiable and traceable technical path for information sharing in complex network environments. At the same timeby optimizing the integration mode of the consensus mechanism and federated learningthe system communication overhead and response delay have been significantly reducedand the overall operational efficiency has been improved.
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Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
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Funding
The research is supported by: Scientific research Foundation of the Education Department of Hubei Provinceunder Grant NO. B2020301; the major project Foundation of philosophy and social sciences research in Hubei Province under Grant NO. 20ZD107; And the Fundation of excellent young and middle-aged scientific and Technological Innovation Team project in Hubei Province under Grant NO. T201943.
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D.X. processed the numerical attribute linear programming of communication big dataand the mutual information feature quantity of communication big data numerical attribute was extracted by the cloud extended distributed feature fitting method. J.L. Combined with fuzzy C-means clustering and linear regression analysisthe statistical analysis of big data numerical attribute feature information was carried outand the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. D.X. and Z.W.R. did the experimentsrecorded dataand created manuscripts. All authors read and approved the final manuscript.
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XuD.LiJ. & RenZ. Supply chain information security sharing technology based on blockchain consensus algorithm and federated learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-46101-z
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DOI: https://doi.org/10.1038/s41598-026-46101-z