  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.
