  Federated Learning (FL) is a new machine learning framework, which enables
millions of participants to collaboratively train machine learning model
without compromising data privacy and security. Due to the independence and
confidentiality of each client, FL does not guarantee that all clients are
honest by design, which makes it vulnerable to adversarial attack naturally. In
this paper, we focus on dynamic backdoor attacks under FL setting, where the
goal of the adversary is to reduce the performance of the model on targeted
tasks while maintaining a good performance on the main task, current existing
studies are mainly focused on static backdoor attacks, that is the poison
pattern injected is unchanged, however, FL is an online learning framework, and
adversarial targets can be changed dynamically by attacker, traditional
algorithms require learning a new targeted task from scratch, which could be
computationally expensive and require a large number of adversarial training
examples, to avoid this, we bridge meta-learning and backdoor attacks under FL
setting, in which case we can learn a versatile model from previous
experiences, and fast adapting to new adversarial tasks with a few of examples.
We evaluate our algorithm on different datasets, and demonstrate that our
algorithm can achieve good results with respect to dynamic backdoor attacks. To
the best of our knowledge, this is the first paper that focus on dynamic
backdoor attacks research under FL setting.
