  Federated learning (FL) is a fast-developing technique that allows multiple
workers to train a global model based on a distributed dataset. Conventional FL
(FedAvg) employs gradient descent algorithm, which may not be efficient enough.
Momentum is able to improve the situation by adding an additional momentum step
to accelerate the convergence and has demonstrated its benefits in both
centralized and FL environments. It is well-known that Nesterov Accelerated
Gradient (NAG) is a more advantageous form of momentum, but it is not clear how
to quantify the benefits of NAG in FL so far. This motives us to propose
FedNAG, which employs NAG in each worker as well as NAG momentum and model
aggregation in the aggregator. We provide a detailed convergence analysis of
FedNAG and compare it with FedAvg. Extensive experiments based on real-world
datasets and trace-driven simulation are conducted, demonstrating that FedNAG
increases the learning accuracy by 3-24% and decreases the total training time
by 11-70% compared with the benchmarks under a wide range of settings.
