Abstract: Clustering clients and conducting federated learning within clusters is an effective method to mitigate the poor perfor-mance of traditional federated learning algorithms in non-independently and identically distributed(Non-IID) data scenarios.Such methods primarily utilize the parameters of a client’s local model to characterize data features,and evaluate similarity through the “distance” between parameters,thereby realizing client clustering.However,due to the permutation invariance of neurons in neural networks,this could lead to inaccurate clustering results.Moreover,these methods typically require a predetermined number of clusters,which might result in unreasonable clusters,or they may require clustering during the algorithmic iterative process,lea-ding to substantial communication overhead.After in-depth analysis of the shortcomings of existing methods,a novel federated learning algorithm named FedRCD is proposed.This algorithm combines autoencoders and K-Means algorithms,directly extracting distribution information from a client’s dataset to represent its characteristics,thereby reducing reliance on model parameters.FedRCD also organizes the relationships between clients into a graph structure,and employs the Louvain algorithm to construct client clustering relationships.This process does not require pre-setting the number of clusters,which makes the clustering results more reasonable.Experimental results show that FedRCD can more effectively unearth latent clustering relationships between clients.In a variety of Non-IID data scenarios,compared to other federated learning algorithms,it significantly improves the training effect of neural networks.On the CIFAR10 dataset,the accuracy of FedRCD surpasses the classical FedAvg algorithm by 37.08%,and even outperforms the newly released FeSEM algorithm by 1.89%,demonstrating superior fairness performance.

Key words: Federated learning, Non-IID, Distribution extraction, Community detection, Louvain algorithm