融合秘密分享技术的双重纵向联邦学习框架

Dual vertical federated learning framework incorporating secret sharing technology

针对水电行业中的跨媒体数据融合建模和隐私保护的问题,提出一种融合秘密分享技术的双重纵向联邦学习框架。首先,将各参与方节点进行分层,其中底层节点负责预建模,中间层节点负责预模型汇总与优化,中心方节点则生成最终模型;其次,为强化数据隐私性保护和防范推理攻击,引入基于秘密分享技术的中间参数保护机制,在该机制中数据拥有者与模型训练方之间的通信数据被碎片化分割,以确保模型参数与训练者的对应关系的隐蔽性,提高攻击者推理攻击的难度;最后,为优化联邦学习的模型聚合过程,引入基于信息量差异的节点评估机制,该机制综合考虑节点的相异度和数据量,精细权衡不同节点在模型聚合中的权重,并剔除疑似的恶意节点的贡献,从而优化模型的性能和收敛速度。实验数据集选取国电大渡河流域水电开发有限公司的真实数据,结果显示:基于秘密分享技术的中间参数保护机制相较于差分隐私保护机制,收敛过程更稳定,收敛速度提升约14.6%;引入基于信息量差异的节点评估机制,相较于联邦平均算法,收敛速度提升约13.5%。可见,所提框架解决了水电数据的跨媒体数据融合建模问题,并具有数据隐私保护和模型收敛加速的优势。

To address the issues of cross-media data fusion modeling and privacy protection in the hydropower industry,a dual vertical federated learning framework incorporating secret sharing technology was proposed.First,the participant nodes were stratified,with lower-tier nodes responsible for preliminary modeling,intermediate-tier nodes overseeing premodel aggregation and optimization,and central nodes generating the final model.Then,in order to strengthen data privacy protection and prevent inference attacks,an intermediate parameter protection mechanism based on secret sharing technology was introduced,the communication data between the data owner and the model trainer was fragmented and divided,which ensured the covertness of the correspondence between the model parameters and the trainers,thereby increasing the complexity of inference attacks.Finally,in order to optimize the model aggregation process of federated learning,a node evaluation mechanism based on the disparity in information quantities was introduced,in which the node dissimilarity and data volume were assessed comprehensively.The weights of different nodes in model aggregation were finely adjusted,and the contribution of suspected malicious nodes was eliminated,thus optimizing the performance and convergence speed of the model.The real data of Guodian Dadu River Basin Hydropower Development Company Limited was selected for experiments.The results showed that:the intermediate parameter protection mechanism based on secret sharing technology was more stable during the convergence process and improves the convergence speed by approximately 14.6%compared to the differential privacy protection mechanism;by incorporating a node evaluation mechanism based on information disparity,the convergence speed was increased by approximately 13.5%compared to the federated averaging algorithm.It is verified that the proposed framework addresses the issue of cross-media data fusion modeling for hydropower data,and it possesses the advantages of data privacy protection and model convergence acceleration.