Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2301.05250
