  Federated learning aims to jointly learn statistical models over massively
distributed remote devices. In this work, we propose FedDANE, an optimization
method that we adapt from DANE, a method for classical distributed
optimization, to handle the practical constraints of federated learning. We
provide convergence guarantees for this method when learning over both convex
and non-convex functions. Despite encouraging theoretical results, we find that
the method has underwhelming performance empirically. In particular, through
empirical simulations on both synthetic and real-world datasets, FedDANE
consistently underperforms baselines of FedAvg and FedProx in realistic
federated settings. We identify low device participation and statistical device
heterogeneity as two underlying causes of this underwhelming performance, and
conclude by suggesting several directions of future work.
