Abstract: 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.

Key words: Consensus algorithm, Federal learning, Model election, Data privacy, Heterogeneous cluster