Abstract
Federated learning is a distributed machine learning framework that allows participants to keep their privacy data locally. Traditional federated learning coordinates participants collaboratively train a powerful global model. However, this process has several problems: it cannot meet the heterogeneous model’s requirements, and it cannot resist poisoning attacks and single-point-of-failure. In order to resolve these issues, we proposed a heterogeneous federated learning algorithm based on blockchain and model distillation. The problem of fully heterogeneous models that are hard to aggregate in the central server can be solved by leveraging model distillation technology. Moreover, blockchain replaces the central server in federated learning to solve the single-point-of-failure problem. The validation algorithm is combined with cross-validation, which helps federated learning to resist poison attacks. The extensive experimental results demonstrate that HBMD-FL can resist poisoning attacks while losing less than 3\(\%\) of model accuracy, and the communication consumption significantly outperformed the comparison algorithm.
This work is partially supported by Natural Science Foundation of China (62206238), Natural Science Foundation of Jiangsu Province (Grant No. BK20220562), Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant No. 22KJB520010), Future Network Scientific Research Fund Project (FNSRFP-2021-YB-47), Yangzhou City-Yangzhou University Science and Technology Cooperation Fund Project (YZ2021158).
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Li, Y., Zhang, J., Zhu, J., Li, W. (2022). HBMD-FL: Heterogeneous Federated Learning Algorithm Based on Blockchain and Model Distillation. In: Chen, J., He, D., Lu, R. (eds) Emerging Information Security and Applications. EISA 2022. Communications in Computer and Information Science, vol 1641. Springer, Cham. https://doi.org/10.1007/978-3-031-23098-1_9
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