Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. (arXiv:2209.15245v1 [cs.LG])

Due to limited communication capacities of edge devices, most existing
federated learning (FL) methods randomly select only a subset of devices to
participate in training for each communication round. Compared with engaging
all the available clients, the random-selection mechanism can lead to
significant performance degradation on non-IID (independent and identically
distributed) data. In this paper, we show our key observation that the
essential reason resulting in such performance degradation is the
class-imbalance of the grouped data from randomly selected clients. …

arxiv class-imbalance client federated learning sampling