Abstract
Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism.
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Acknowledgements
This work is supported by the Rector proquality grant at the Silesian University of Technology, Poland No. 09/010/RGJ22/0067.
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KP: Conceptualization, Methodology, Software, Data curation, Validation, Investigation, Visualization, Writing-original draft. DP: Conceptualization, Methodology, Data curation, Validation, Investigation, Writing-review and editing. GS: Methodology, Validation, Writing-review and editing. JC-WL: Methodology, Writing-review and editing.
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Prokop, K., Połap, D., Srivastava, G. et al. Blockchain-based federated learning with checksums to increase security in Internet of Things solutions. J Ambient Intell Human Comput 14, 4685–4694 (2023). https://doi.org/10.1007/s12652-022-04372-0
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DOI: https://doi.org/10.1007/s12652-022-04372-0