  Federated learning has burgeoned recently in machine learning, giving rise to
a variety of research topics. Popular optimization algorithms are based on the
frameworks of the (stochastic) gradient descent methods or the alternating
direction method of multipliers. In this paper, we deploy an exact penalty
method to deal with federated learning and propose an algorithm, FedEPM, that
enables to tackle four critical issues in federated learning: communication
efficiency, computational complexity, stragglers' effect, and data privacy.
Moreover, it is proven to be convergent and testified to have high numerical
performance.
