Advances in distributed machine learning can empower future communications and networking. The emergence of federated learning (FL) has provided an efficient framework for distributed machine learning, which, however, still faces many security challenges. Among them, model poisoning attacks have a significant impact on the security and performance of FL. Given that there have been many studies focusing on defending against model poisoning attacks, it is necessary to survey the existing work and provide insights to inspire future research. In this paper, we first classify defense mechanisms for model poisoning attacks into two categories: evaluation methods for local model updates and aggregation methods for the global model. Then, we analyze some of the existing defense strategies in detail. We also discuss some potential challenges and future research directions. To the best of our knowledge, we are the first to survey defense methods for model poisoning attacks in FL.
翻译:分布式机器学习的进展可以增强未来的通信和网络联系。联合会学习的出现为分布式机器学习提供了一个有效的框架,然而,这种学习仍面临许多安全挑战。其中,中毒示范袭击对自由党的安全和工作产生了重大影响。鉴于已经进行了许多侧重于防范中毒示范袭击的研究,有必要对现有工作进行调查,并提供见解,以激发未来的研究。在本文件中,我们首先将中毒示范袭击的防御机制分为两类:地方模式更新的评估方法和全球模式的汇总方法。然后,我们详细分析一些现有的防御战略。我们还讨论了一些潜在挑战和未来的研究方向。据我们所知,我们首先调查了自由党中中毒示范袭击的防御方法。