  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. Our method exploits clipping
and smoothing on model parameters to control the global model smoothness, which
yields a sample-wise robustness certification on backdoors with limited
magnitude. Our certification also specifies the relation to federated learning
parameters, such as poisoning ratio on instance level, number of attackers, and
training iterations. Practically, we conduct comprehensive experiments across a
range of federated datasets, and provide the first benchmark for certified
robustness against backdoor attacks in federated learning. Our code is
available at https://github.com/AI-secure/CRFL.
