Communication-Efficient Federated Learning with Accelerated Client Gradient

Understand

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.