Abstract: Clustering federated learning is often used to solve the problem of decreasing accuracy caused by data heterogeneity in federated learning.The idea is to group clients with similar data distributions into the same cluster using clustering algorithms,and then train a cluster model specifically for that distribution.However,in practical applications,it is challenging to achieve ideal results because the training and test datasets used by the local clients may not match the data distribution of the cluster model,leading to a significant drop in inter-cluster accuracy.To improve the accuracy of the cluster model in clustering federatedlear-ning,this paper proposes two solutions.The first is adaptive weighted clustering federated learning(AWCFL),which incorporates models from other clusters during intra-cluster aggregation,enabling the cluster model to learn from other distributions and effectively improve inter-cluster accuracy.The second solution is multi-distribution clustering federated learning(MCFL),which synchronizes the cluster model with each client,allowing clients to choose the appropriate model to use.Compared with the first solution,intra-cluster accuracy remains unaffected in MCFL,while inter-cluster accuracy is significantly improved.To evaluate the proposed solutions,experiments are conducted on the Mnist and EMnist datasets.Compared with IFCA,clustered federated lear-ning(CFL) and FedAvg,the accuracy rate between clusters is significantly improved.

Key words: Federated learning, Clustering, Aggregation optimization, Data distribution