  In recent years, mobile clients' computing ability and storage capacity have
greatly improved, efficiently dealing with some applications locally. Federated
learning is a promising distributed machine learning solution that uses local
computing and local data to train the Artificial Intelligence (AI) model.
Combining local computing and federated learning can train a powerful AI model
under the premise of ensuring local data privacy while making full use of
mobile clients' resources. However, the heterogeneity of local data, that is,
Non-independent and identical distribution (Non-IID) and imbalance of local
data size, may bring a bottleneck hindering the application of federated
learning in mobile edge computing (MEC) system. Inspired by this, we propose a
cluster-based clients selection method that can generate a federated virtual
dataset that satisfies the global distribution to offset the impact of data
heterogeneity and proved that the proposed scheme could converge to an
approximate optimal solution. Based on the clustering method, we propose an
auction-based clients selection scheme within each cluster that fully considers
the system's energy heterogeneity and gives the Nash equilibrium solution of
the proposed scheme for balance the energy consumption and improving the
convergence rate. The simulation results show that our proposed selection
methods and auction-based federated learning can achieve better performance
with the Convolutional Neural Network model (CNN) under different data
distributions.
