  Due to its distributed methodology alongside its privacy-preserving features,
Federated Learning (FL) is vulnerable to training time adversarial attacks. In
this study, our focus is on backdoor attacks in which the adversary's goal is
to cause targeted misclassifications for inputs embedded with an adversarial
trigger while maintaining an acceptable performance on the main learning task
at hand. Contemporary defenses against backdoor attacks in federated learning
require direct access to each individual client's update which is not feasible
in recent FL settings where Secure Aggregation is deployed. In this study, we
seek to answer the following question, Is it possible to defend against
backdoor attacks when secure aggregation is in place?, a question that has not
been addressed by prior arts. To this end, we propose Meta Federated Learning
(Meta-FL), a novel variant of federated learning which not only is compatible
with secure aggregation protocol but also facilitates defense against backdoor
attacks. We perform a systematic evaluation of Meta-FL on two classification
datasets: SVHN and GTSRB. The results show that Meta-FL not only achieves
better utility than classic FL, but also enhances the performance of
contemporary defenses in terms of robustness against adversarial attacks.
