基于模型准确率的链上去中心化联邦学习模型

Centralized federated learning model based on model accuracy

现有的联邦学习存在恶意中央服务器和恶意参与者发布虚假数据毒害模型等问题。针对此情况,该文提出了一种去中心化的联邦学习模型,该模型将聚合工作由中央服务器移至参与者本地,各个参与者依据聚合算法将训练之后的模型参数写入交易,生成区块发布到区块链网络中。采用一种基于模型准确率的Byzantine容错共识算法构建共识小组,通过建立节点信息表实现节点动态加入。对所提的链上去中心化联邦学习模型的吞吐量、时延等性能进行了相关测试,结果表明:在相同条件下,基于模型准确率的高性能Byzantine容错共识算法相较于传统的Byzantine容错共识算法,吞吐量提升60%,系统平均时延从6 s减少到1 s。

Existing federated learning models have problems due to malicious central servers and malicious participants publishing false data that poisons the model. A decentralized federated learning model was developed to address these problems by moving the aggregation work from the central server to the participants’ computers. Each participant uses the aggregation algorithm to write the trained model parameters into the transaction and generates blocks that are then published to the blockchain network. A Byzantine fault-tolerant consensus algorithm based on model accuracy is used to build a consensus group and the nodes are dynamically joined by establishing a node information table. The results show that under the same conditions, compared with the traditional Byzantine fault-tolerant consensus algorithm, the throughput of the high-performance Byzantine fault-tolerant consensus algorithm based on model accuracy is increased by 60%, and the average system delay is reduced from 6 s to 1 s.