  Federated learning (FL) has been demonstrated to be susceptible to backdoor
attacks. However, existing academic studies on FL backdoor attacks rely on a
high proportion of real clients with main task-related data, which is
impractical. In the context of real-world industrial scenarios, even the
simplest defense suffices to defend against the state-of-the-art attack, 3DFed.
A practical FL backdoor attack remains in a nascent stage of development.
  To bridge this gap, we present DarkFed. Initially, we emulate a series of
fake clients, thereby achieving the attacker proportion typical of academic
research scenarios. Given that these emulated fake clients lack genuine
training data, we further propose a data-free approach to backdoor FL.
Specifically, we delve into the feasibility of injecting a backdoor using a
shadow dataset. Our exploration reveals that impressive attack performance can
be achieved, even when there is a substantial gap between the shadow dataset
and the main task dataset. This holds true even when employing synthetic data
devoid of any semantic information as the shadow dataset. Subsequently, we
strategically construct a series of covert backdoor updates in an optimized
manner, mimicking the properties of benign updates, to evade detection by
defenses. A substantial body of empirical evidence validates the tangible
effectiveness of DarkFed.
