FedCOA: an efficient cluster optimization for federated learning

Federated learning is a novel distributed machine learning framework based on data privacy protection. In practical applications, there are often significant differences in data distribution among different clients in federated learning, which can lead to a decrease in the performance of federated learning models. This phenomenon is caused by the problem of independent and identically distributed data. To address the issue of independent and identically distributed data, this paper proposes a novel federated learning algorithm called FedCOA (Federated Cluster Optimization Algorithm). The FedCOA algorithm iteratively partitions the clients into different client clusters based on the similarity of their data distributions. Clients with similar data distributions within a cluster collaborate with each other, there by enhancing model generalization and reducing overfitting. In the simulation experiments, under the same dataset, the FedCOA algorithm achieves an approximately 10% improvement in accuracy compared to the FedAvg algorithm, and2% improvement compared to the CFL algorithm. Additionally, this paper conducts comparative experiments under different degrees of Non-Independent and Identically Distributed data distribution. The experimental results demonstrate that FedCOA can enhance model accuracy while effectively addressing issues such as the decrease in model accuracy and the inability of the model to converge caused by Non-Independent and Identically Distributed data distribution.