Prototype Helps Federated Learning: Towards Faster Convergence

Understand

Federated learning (FL) is a distributed machine learning technique in which multiple clients cooperate to train a shared model without exchanging their raw data.

• However, heterogeneity of data distribution among clients usually leads to poor model inference.
• In this paper, a prototype-based federated learning framework is proposed, which can achieve better inference performance with only a few changes to the last global iteration of the typical federated learning process.
• In the last iteration, the server aggregates the prototypes transmitted from distributed clients and then sends them back to local clients for their respective model inferences.