  Federated neuromorphic learning (FedNL) leverages event-driven spiking neural
networks and federated learning frameworks to effectively execute intelligent
analysis tasks over amounts of distributed low-power devices but also perform
vulnerability to poisoning attacks. The threat of backdoor attacks on
traditional deep neural networks typically comes from time-invariant data.
However, in FedNL, unknown threats may be hidden in time-varying spike signals.
In this paper, we start to explore a novel vulnerability of FedNL-based systems
with the concept of time division multiplexing, termed Spikewhisper, which
allows attackers to evade detection as much as possible, as multiple malicious
clients can imperceptibly poison with different triggers at different
timeslices. In particular, the stealthiness of Spikewhisper is derived from the
time-domain divisibility of global triggers, in which each malicious client
pastes only one local trigger to a certain timeslice in the neuromorphic
sample, and also the polarity and motion of each local trigger can be
configured by attackers. Extensive experiments based on two different
neuromorphic datasets demonstrate that the attack success rate of Spikewispher
is higher than the temporally centralized attacks. Besides, it is validated
that the effect of Spikewispher is sensitive to the trigger duration.
