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.

We demonstrate that Fed-Focal Loss effectively handles class imbalance without requiring knowledge of the global data distribution, while maintaining the privacy guarantees of federated learning. Our experimental results show improved performance on imbalanced classification tasks compared to traditional federated learning approaches, particularly for minority classes.

The proposed method is evaluated on multiple datasets and shows consistent improvements in metrics such as balanced accuracy and F1-score, making it particularly suitable for real-world federated learning applications where data imbalance is common.