Meta Federated Learning

Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time backdoor attacks. Contemporary defenses against backdoor attacks in FL 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?”. To this end, we propose Meta Federated Learning (Meta-FL), a novel variant of FL which not only is compatible with secure aggregation protocol but also facilitates defense against backdoor attacks.