基于联盟链的隐私保护联邦学习框架

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中图分类号： TP309 文献标志码： A

文章编号： 1671-6841（2025）04-0023-07

Abstract： Aiming at the shortcomings of existing federated learning models in privacy protection and poisoning attack defense， a privacy-preserving federated learning framework based on consortium chain was proposed. Firstly， the framework employed homomorphic encryption techniques and Laplacian noise to ensure data privacy， effectively preserving the confidentiality of data from various parties during model training. Secondly， through the consensus protocol of the consortium chain and a model aggregation algorithm， distinct gradient aggregation weights were assigned to different participants， mitigating the impact of malicious parties on model aggregation and enhancing the robustness of the model. The experimental results conducted on the MNIST and Fashion-MNIST datasets demonstrated that even with a malicious participant ratio up to 40% ， the proposed framework could still maintain high model accuracy with label reversal attack and backdoor attack.

Key words： federated learning; privacy protection; poisoning attack; consortium chain; model aggregation

0 引言

在数据驱动时代，机器学习技术赋予了计算机从庞大数据集中提炼人类行为经验的能力。（剩余11662字）