  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.
