  Due to the greatly improved capabilities of devices, massive data, and
increasing concern about data privacy, Federated Learning (FL) has been
increasingly considered for applications to wireless communication networks
(WCNs). Wireless FL (WFL) is a distributed method of training a global deep
learning model in which a large number of participants each train a local model
on their training datasets and then upload the local model updates to a central
server. However, in general, non-independent and identically distributed
(non-IID) data of WCNs raises concerns about robustness, as a malicious
participant could potentially inject a "backdoor" into the global model by
uploading poisoned data or models over WCN. This could cause the model to
misclassify malicious inputs as a specific target class while behaving normally
with benign inputs. This survey provides a comprehensive review of the latest
backdoor attacks and defense mechanisms. It classifies them according to their
targets (data poisoning or model poisoning), the attack phase (local data
collection, training, or aggregation), and defense stage (local training,
before aggregation, during aggregation, or after aggregation). The strengths
and limitations of existing attack strategies and defense mechanisms are
analyzed in detail. Comparisons of existing attack methods and defense designs
are carried out, pointing to noteworthy findings, open challenges, and
potential future research directions related to security and privacy of WFL.
