Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data

arXiv:2404.14933v1 Announce Type: new
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. …

abstract anomaly anomaly detection arxiv challenge challenges concerns cs.ai cs.lg data data sharing detection dynamic fed fin financial open-world organizations outlier practical privacy robust tabular tabular data type world