FL_Raft:基于联邦学习模型的选举共识方案

FL_Raft:Election Consensus Programme Based on Federated Learning Model

针对异构集群中Raft共识算法的投票分裂和领导者频繁更换造成的吞吐量低、共识时延高和安全性低等问题,提出了一种基于联邦学习模型的Raft选举共识方案FL_Raft。首先,联邦学习聚合运行于每轮领导者迭代后,调用节点的本地特征数据,通过联邦学习训练模型筛选高性能节点组;其次,建立基于行为的权益计算模型,对集群中每个节点的权益值进行动态调整;最后,建立权益选举模型,由队列选举准领导者节点,选取出的节点经全体节点投票选举后成为最终领导者节点。实验结果表明,在保证各节点数据隐私性的前提下,相比Raft,FL_Raft的选举时延降低了50%,领导者可靠性达到95%以上,共识时延缩短了20%,吞吐量提高了13%。FL_Raft共识算法保证了选举的效率和安全,提高了集群的稳定性和领导者的可用性。

Aiming at the problems of low throughput,high consensus delay and low security caused by vote splitting and frequent leader change of Raft consensus algorithm in heterogeneous clusters,a Raft election consensus programme FL_Raft based on fe-derated learning model is proposed.First,federated learning aggregation runs after each leader iteration,invokes the local characteristic data of nodes,and filters high-performance node groups through the federated learning training model.Secondly,a beha-vior-based equity calculation model is established to dynamically adjust the equity value of each node in the cluster.Finally,the equity election model is established to elect the quasi leader node,which becomes the final leader node after all nodes vote.Experimental results show that under the premise of ensuring the data privacy of each node,compared with Raft,the FL_Raft election delay reduces by 50%,the leader reliability is more than 95%,the consensus delay reduces by 20%,and the throughput increases by 13%.The FL_Raft consensus algorithm ensures the efficiency and security of the election,and improves the stability of the cluster and the availability of leaders.