  Decentralized federated learning (DFL) is an effective approach to train a
deep learning model at multiple nodes over a multi-hop network, without the
need of a server having direct connections to all nodes. In general, as long as
nodes are connected potentially via multiple hops, the DFL process will
eventually allow each node to experience the effects of models from all other
nodes via either direct connections or multi-hop paths, and thus is able to
train a high-fidelity model at each node. We consider an effective attack that
uses jammers to prevent the model exchanges between nodes. There are two attack
scenarios. First, the adversary can attack any link under a certain budget.
Once attacked, two end nodes of a link cannot exchange their models. Secondly,
some jammers with limited jamming ranges are deployed in the network and a
jammer can only jam nodes within its jamming range. Once a directional link is
attacked, the receiver node cannot receive the model from the transmitter node.
We design algorithms to select links to be attacked for both scenarios. For the
second scenario, we also design algorithms to deploy jammers at optimal
locations so that they can attack critical nodes and achieve the highest impact
on the DFL process. We evaluate these algorithms by using wireless signal
classification over a large network area as the use case and identify how these
attack mechanisms exploits various learning, connectivity, and sensing aspects.
We show that the DFL performance can be significantly reduced by jamming
attacks launched in a wireless network and characterize the attack surface as a
vulnerability study before the safe deployment of DFL over wireless networks.
