  Edge devices in federated learning usually have much more limited computation
and communication resources compared to servers in a data center. Recently,
advanced model compression methods, like the Lottery Ticket Hypothesis, have
already been implemented on federated learning to reduce the model size and
communication cost. However, Backdoor Attack can compromise its implementation
in the federated learning scenario. The malicious edge device trains the client
model with poisoned private data and uploads parameters to the center,
embedding a backdoor to the global shared model after unwitting aggregative
optimization. During the inference phase, the model with backdoors classifies
samples with a certain trigger as one target category, while shows a slight
decrease in inference accuracy to clean samples. In this work, we empirically
demonstrate that Lottery Ticket models are equally vulnerable to backdoor
attacks as the original dense models, and backdoor attacks can influence the
structure of extracted tickets. Based on tickets' similarities between each
other, we provide a feasible defense for federated learning against backdoor
attacks on various datasets.
