PyTorch Code for Feature Boosting, Suppression, and Diversification for Fine-Grained Visual Classification

A framework for experimenting with privacy-preserving mechanisms in federated learning. This toolkit enables comparison between local training, standard federated learning, feature suppression, and differential privacy approaches. Includes tools for data preparation, model training, result visualization, and privacy-utility tradeoff analysis.