  Federated learning (FL) has become de facto framework for collaborative
learning among edge devices with privacy concern. The core of the FL strategy
is the use of stochastic gradient descent (SGD) in a distributed manner. Large
scale implementation of FL brings new challenges, such as the incorporation of
acceleration techniques designed for SGD into the distributed setting, and
mitigation of the drift problem due to non-homogeneous distribution of local
datasets. These two problems have been separately studied in the literature;
whereas, in this paper, we show that it is possible to address both problems
using a single strategy without any major alteration to the FL framework, or
introducing additional computation and communication load. To achieve this
goal, we propose FedADC, which is an accelerated FL algorithm with drift
control. We empirically illustrate the advantages of FedADC.
