论文标题
无线临时联合学习反对模型中毒攻击的弹性
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks
论文作者
论文摘要
无线临时联合学习（WAFL）是一个完全分散的协作机器学习框架，该框架由机会主义的移动节点组织。与传统的联合学习相比，WAFL通过与他人弱同步模型参数来执行模型训练，这表明了对攻击者注入的中毒模型的极大韧性。在本文中，我们通过制定有毒模型与合法模型之间的力量平衡来对WAFL对模型中毒攻击的弹性进行理论分析。根据我们的实验，我们确认节点直接遇到了攻击者已被某种程度上妥协的中毒模型，但其他节点表现出很大的弹性。更重要的是，在攻击者离开网络后，所有节点终于发现了更强大的模型参数与中毒模型结合在一起。大多数攻击经验的病例的准确性比无攻击经验的案例更高。
Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great resilience to a poisoned model injected by an attacker. In this paper, we provide our theoretical analysis of the WAFL's resilience against model poisoning attacks, by formulating the force balance between the poisoned model and the legitimate model. According to our experiments, we confirmed that the nodes directly encountered the attacker has been somehow compromised to the poisoned model but other nodes have shown great resilience. More importantly, after the attacker has left the network, all the nodes have finally found stronger model parameters combined with the poisoned model. Most of the attack-experienced cases achieved higher accuracy than the no-attack-experienced cases.
