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.
翻译:DFL 进程将最终允许每个节点通过直接连接或多节点路径来体验所有其他节点的模型的影响,从而能够在每个节点上训练一个高纤维性能模型。我们考虑一种有效的攻击,即利用干扰器来防止节点之间的模式交换。有两种攻击情景。首先,敌人可以在特定预算下攻击任何链接。一旦被攻击,一个链点的终端节点将无法通过多个跳点进行交换。第二,一个链点的终端节点将有可能通过多跳点连接连接起来,DFL进程将最终允许每个节点通过直接连接或多跳点路径来体验所有其他节点的模型的影响,从而能够在每个节点上训练一个高纤维性能模型。我们考虑一种有效的攻击,在两个节点之间使用干扰器来防止模式交流。我们设计了两种攻击情景前的设置算法。在第二个假设中,敌人可以攻击任何安全网络的连接点,一旦被攻击,一个链点就会在攻击后,我们设计一个信号值在最大节点上安装一个节点的节点的节点,通过最精确的节点的节点的节点上,我们使用这些节点的节点的节点的节点,然后通过学习的节点的节点的节点的节点来进行计算,我们使用这些节点的节点的节点的节点的节点的节点来显示的节点的节点的节点的节点,我们算来显示的节点,我们使用这些节点的节点,然后在攻击。