  Federated Distillation (FD) is a novel and promising distributed machine
learning paradigm, where knowledge distillation is leveraged to facilitate a
more efficient and flexible cross-device knowledge transfer in federated
learning. By optimizing local models with knowledge distillation, FD
circumvents the necessity of uploading large-scale model parameters to the
central server, simultaneously preserving the raw data on local clients.
Despite the growing popularity of FD, there is a noticeable gap in previous
works concerning the exploration of poisoning attacks within this framework.
This can lead to a scant understanding of the vulnerabilities to potential
adversarial actions. To this end, we introduce FDLA, a poisoning attack method
tailored for FD. FDLA manipulates logit communications in FD, aiming to
significantly degrade model performance on clients through misleading the
discrimination of private samples. Through extensive simulation experiments
across a variety of datasets, attack scenarios, and FD configurations, we
demonstrate that LPA effectively compromises client model accuracy,
outperforming established baseline algorithms in this regard. Our findings
underscore the critical need for robust defense mechanisms in FD settings to
mitigate such adversarial threats.
