decentralized-federated-learning

D-FROST introduces a decentralized federated prompt-tuning algorithm using optimal transport to address challenges with non-IID and imbalanced data, ensuring convergence and effectiveness for foundation models.

This paper introduces BackDFL, a unified benchmark for systematically evaluating backdoor attacks and defenses in decentralized federated learning, revealing critical failure modes in current robustness methods.