Fed-Focal Loss for imbalanced data classification in Federated Learning
Federated Learning has emerged as a promising paradigm for training machine learning models while preserving data privacy. However, handling class imbalance in federated settings remains challenging. This work introduces Fed-Focal Loss, a novel approach that adapts focal loss for federated learning scenarios to address data imbalance across distributed clients.
Nov 1, 2020