Abstract: With the development of the Internet of Things, collaborative work between multiple devices is becoming increasingly common. However, in the process of data sharing, user privacy may face the risk of data theft and tampering. Existing FL methods rely on mobile edge computing (MEC) servers for model aggregation, and have problems with trust, security threats, and single points of failure. To solve these problems, a new multiparty data sharing scheme based on blockchain is proposed, in which a decentralized verification mechanism and a consensus mechanism inspired by proof of stake (PoS) are introduced. The decentralized verification mechanism ensures the legitimacy of each local model update by evaluating node behavior and voting, and only legitimate updates are used for global model building. In the process of model construction, homomorphic encryption and key sharing techniques are used to encrypt the local model parameters to ensure the security integrity of model parameters in the process of transmission and aggregation. The PoS consensus mechanism rewards honest behavior devices, increasing their chances of becoming block generators. In addition, the cache mechanism of information search is introduced to reduce the number of multiparty search. The data sharing scheme has been verified to enhance data security.

Key words: federal learning, blockchain, decentralized verification, proof of interest, multiparty data sharing

摘要： 随着物联网的发展，多设备之间的协同工作变得越来越普遍.然而，在数据共享过程中用户隐私可能面临数据被盗取和篡改的风险.现有的联邦学习(federal learning, FL)方法依赖于移动边缘计算(mobile edge computing, MEC)服务器进行模型聚合，存在信任、安全威胁和单点故障等问题.为解决这些问题，以区块链为底层架构，提出了一种新型的多方数据共享方案，在方案中引入了去中心化验证机制和权益证明(PoS)启发的共识机制.去中心化验证机制通过评估节点行为并投票，确保每个本地模型更新的合法性，只有合法的更新才会被用于全局模型构建.模型构建过程使用同态加密和密钥共享技术对本地模型参数进行加密，确保模型参数在传输和聚合过程中的安全性与完整性.PoS共识机制奖励诚实行为设备，增加其成为区块生成机会.此外，通过引入信息查找的缓存机制减少了多方搜索次数.经过验证，该数据共享方案在数据安全方面有所增强.

关键词: 联邦学习, 区块链, 去中心化验证, 权益证明, 多方数据共享