Federated learning (FL) is a distributed machine learning paradigm with high
efficiency and low communication load, only transmitting parameters or
gradients of network. However, the non-independent and identically distributed
(Non-IID) data characteristic has a negative impact on this paradigm.
Furthermore, the heterogeneity of communication quality will significantly
affect the accuracy of parameter transmission, causing a degradation in the
performance of the FL system or even preventing its convergence. This letter
proposes a dual-segment clustering (DSC) strategy, which first clusters the
clients according to the heterogeneous communication conditions and then
performs a second clustering by the sample size and label distribution, so as
to solve the problem of data and communication heterogeneity. Experimental
results show that the DSC strategy proposed in this letter can improve the
convergence rate of FL, and has superiority on accuracy in a heterogeneous
environment compared with the classical algorithm of cluster.

提出了一种双段聚类（DSC）策略，通过首先根据异构通信条件对客户端进行聚类，然后通过样本大小和标签分布进行第二次聚类，以解决数据和通信异构性问题。实验结果表明，该策略可以提高联邦学习的收敛速度，在异构环境中相比经典聚类算法具有更高的准确性。