  Federated learning allows distributed devices to collectively train a model
without sharing or disclosing the local dataset with a central server. The
global model is optimized by training and averaging the model parameters of all
local participants. However, the improved privacy of federated learning also
introduces challenges including higher computation and communication costs. In
particular, federated learning converges slower than centralized training. We
propose the server averaging algorithm to accelerate convergence. Sever
averaging constructs the shared global model by periodically averaging a set of
previous global models. Our experiments indicate that server averaging not only
converges faster, to a target accuracy, than federated averaging (FedAvg), but
also reduces the computation costs on the client-level through epoch decay.
