Abstract
Anomaly detection in real-world scenarios poses challenges due to dynamic and often unknown anomaly distributions, requiring robust methods that operate under an open-world assumption. This challenge is exacerbated in practical settings, where models are employed by private organizations, precluding data sharing due to privacy and competitive concerns. Despite potential benefits, the sharing of anomaly information across organizations is restricted. This paper addresses the question of enhancing outlier detection within individual organizations without compromising data confidentiality. We propose a novel method leveraging representation learning and federated learning techniques to improve the detection of unknown anomalies. Specifically, our approach utilizes latent representations obtained from client-owned autoencoders to refine the decision boundary of inliers. Notably, only model parameters are shared between organizations, preserving data privacy. The efficacy of our proposed method is evaluated on two standard financial tabular datasets and an image dataset for anomaly detection in a distributed setting. The results demonstrate a strong improvement in the classification of unknown outliers during the inference phase for each organization's model.
Abstract (translated)
在现实场景中，异常检测 poses 挑战，因为动态且往往不确定的异常分布，需要操作在开放世界假设上的稳健方法。在实际场景中，私人组织使用模型，这使得数据无法共享，因为隐私和竞争担忧。尽管存在潜在好处，但组织之间共享异常信息受到限制。本文回答了一个问题：在保留数据机密性的前提下，如何提高组织内部的个人异常检测。我们提出了一种利用客户端自定义的自动编码器的隐式表示来改善未知的异常检测的新方法。具体来说，我们的方法利用客户端自定义的自动
URL
https://arxiv.org/abs/2404.14933
