In a federated learning (FL) system, malicious participants can easily embed
backdoors into the aggregated model while maintaining the model's performance
on the main task. To this end, various defenses, including training stage
aggregation-based defenses and post-training mitigation defenses, have been
proposed recently. While these defenses obtain reasonable performance against
existing backdoor attacks, which are mainly heuristics based, we show that they
are insufficient in the face of more advanced attacks. In particular, we
propose a general reinforcement learning-based backdoor attack framework where
the attacker first trains a (non-myopic) attack policy using a simulator built
upon its local data and common knowledge on the FL system, which is then
applied during actual FL training. Our attack framework is both adaptive and
flexible and achieves strong attack performance and durability even under
state-of-the-art defenses