  Federated learning (FL) is a promising technique that enables a large amount
of edge computing devices to collaboratively train a global learning model. Due
to privacy concerns, the raw data on devices could not be available for
centralized server. Constrained by the spectrum limitation and computation
capacity, only a subset of devices can be engaged to train and transmit the
trained model to centralized server for aggregation. Since the local data
distribution varies among all devices, class imbalance problem arises along
with the unfavorable client selection, resulting in a slow converge rate of the
global model. In this paper, an estimation scheme is designed to reveal the
class distribution without the awareness of raw data. Based on the scheme, a
device selection algorithm towards minimal class imbalance is proposed, thus
can improve the convergence performance of the global model. Simulation results
demonstrate the effectiveness of the proposed algorithm.
