  Federated learning is vulnerable to various attacks, such as model poisoning
and backdoor attacks, even if some existing defense strategies are used. To
address this challenge, we propose an attack-adaptive aggregation strategy to
defend against various attacks for robust federated learning. The proposed
approach is based on training a neural network with an attention mechanism that
learns the vulnerability of federated learning models from a set of plausible
attacks. To the best of our knowledge, our aggregation strategy is the first
one that can be adapted to defend against various attacks in a data-driven
fashion. Our approach has achieved competitive performance in defending model
poisoning and backdoor attacks in federated learning tasks on image and text
datasets.
