基于隐私保护联邦学习与区块链的图像分类方案

Image classification model based on privacy-preserving federated learning and blockchain

传统的中心化图像分类方法受制于数据隐私问题和计算资源限制,无法满足实际需求。现有的联邦学习框架依赖中心服务器,存在单点故障和数据中毒攻击等安全挑战。为解决这些问题,提出了一种面向隐私保护联邦学习与区块链的图像分类方案,通过将联邦学习与区块链技术相结合,实现在分布式环境下进行图像分类任务的可靠性和安全性。图像分类模型通过联邦学习进行训练,并上传至区块链网络进行验证和共识;在分类阶段,模型通过加权组合得到最终分类结果。实验结果表明,该方案在确保用户隐私的同时提高了图像分类的准确度,为解决图像分类中的数据隐私和安全问题提供了一种有效途径,并为提高分类准确性作出了积极探索。

Conventional centralized image classification methods faced challenges due to data privacy issues and limitations in computing resources,rendering them inadequate for practical applications.Existing federated learning frameworks relied on central servers,and there were security challenges such as single points of failure and data poisoning attacks.To address these issues,this paper proposed a novel image classification scheme that combined privacy-preserving federated learning and blockchain technology.The scheme achieved reliability and security in image classification tasks within a distributed environment.This approach trained the image classification model through federated learning and uploaded to the blockchain network for verification and consensus.During the classification phase,the model obtained the final classification result through weighted combination.Experimental results demonstrate that the proposed scheme ensures the accuracy of image classification while protecting user privacy.In conclusion,this paper provides an effective approach to address data privacy and security concerns in image classification,and presents a positive exploration towards improving classification accuracy.