  Federated learning often suffers from slow and unstable convergence due to
the heterogeneous characteristics of participating client datasets. Such a
tendency is aggravated when the client participation ratio is low since the
information collected from the clients has large variations. To address this
challenge, we propose a simple but effective federated learning framework,
which improves the consistency across clients and facilitates the convergence
of the server model. This is achieved by making the server broadcast a global
model with a lookahead gradient. This strategy enables the proposed approach to
convey the projected global update information to participants effectively
without additional client memory and extra communication costs. We also
regularize local updates by aligning each client with the overshot global model
to reduce bias and improve the stability of our algorithm. We provide the
theoretical convergence rate of our algorithm and demonstrate remarkable
performance gains in terms of accuracy and communication efficiency compared to
the state-of-the-art methods, especially with low client participation rates.
The source code is available at our project page.
