桂林电子科技大学学报2025，Vol.45Issue(1)：27-32,6.DOI:10.16725/j.1673-808X.2022259
基于模型相似度的联邦学习本地模型可信计算研究
Research on trusted computing of local model in federated learning based on model similarity
摘要
Abstract
Model training based on blockchain and federated learning is conducive to monitoring the quality of the client's local mod-el while protecting the client's data privacy.However,the risk of malicious client training behavior in the federated learning model-ing process will lead to the degradation of the quality of the local model,which in turn affects the performance of the global model.In response to the above problems,an improved federated learning algorithm based on model similarity was proposed,which im-proved the performance of the federated learning global model through trusted computing of the client's local model.First,the client participated in federated learning for local model training.Then verify the client obtained the local model through the blockchain and model similarity was calculated.Finally,according to the verification results,qualification to participate in the federated learning ag-gregation process is given to the client,so that the client can perform trusted computation of the local model and improve the global model performance of federated learning.Taking the credit evaluation model as an example,the improved federated learning algo-rithm has an accuracy rate of 6.2%higher than the traditional global model,and the convergence is 8 rounds earlier,which effective-ly improves the global model performance of federated learning.关键词
联邦学习/模型相似度/可信计算/本地模型/区块链Key words
federated learning/model similarity/trusted computing/local model/blockchain分类
信息技术与安全科学引用本文复制引用
陈帆,李春海,李晓欢,刘峻瑜..基于模型相似度的联邦学习本地模型可信计算研究[J].桂林电子科技大学学报,2025,45(1):27-32,6.基金项目
广西重点研发计划(AB21196059) （AB21196059）
广西杰出青年基金(2019GXNSFFA245007) （2019GXNSFFA245007）
南宁市重点研发计划(20213028) （20213028）
国家自然科学基金(61762030) （61762030）
