  Federated learning is a distributed machine learning paradigm that trains a
global model for prediction based on a number of local models at clients while
local data privacy is preserved. Class imbalance is believed to be one of the
factors that degrades the global model performance. However, there has been
very little research on if and how class imbalance can affect the global
performance. class imbalance in federated learning is much more complex than
that in traditional non-distributed machine learning, due to different class
imbalance situations at local clients. Class imbalance needs to be re-defined
in distributed learning environments. In this paper, first, we propose two new
metrics to define class imbalance -- the global class imbalance degree (MID)
and the local difference of class imbalance among clients (WCS). Then, we
conduct extensive experiments to analyze the impact of class imbalance on the
global performance in various scenarios based on our definition. Our results
show that a higher MID and a larger WCS degrade more the performance of the
global model. Besides, WCS is shown to slow down the convergence of the global
model by misdirecting the optimization.
