Model-Contrastive Federated Learning

Understand

Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data.

• A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties.
• Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models.
• In this paper, we propose MOON: model-contrastive federated learning.