Communication-Efficient Design for Quantized Decentralized Federated Learning

Understand

Decentralized federated learning (DFL) is a variant of federated learning, where edge nodes only communicate with their one-hop neighbors to learn the optimal model.

• However, as information exchange is restricted in a range of one-hop in DFL, inefficient information exchange leads to more communication rounds to reach the targeted training loss.
• This greatly reduces the communication efficiency.
• In this paper, we propose a new non-uniform quantization of model parameters to improve DFL convergence.