CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

Understand

Federated Learning (FL) as a distributed learning paradigm that aggregates information from diverse clients to train a shared global model, has demonstrated great success.

• However, malicious clients can perform poisoning attacks and model replacement to introduce backdoors into the trained global model.
• Although there have been intensive studies designing robust aggregation methods and empirical robust federated training protocols against backdoors, existing approaches lack robustness certification.
• This paper provides the first general framework, Certifiably Robust Federated Learning (CRFL), to train certifiably robust FL models against backdoors.