Background: Outlier detection (OD) is a key data mining task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.

we introduce FedOD, the first federated learning (FL)-based system designed for general OD algorithms. FedOD effectively overcomes the privacy and efficiency challenges inherent in classical OD algorithms by automatically decomposing these algorithms into a set of basic operators and approximating their behaviors using neural networks.

Dependency: use requirements.txt to install necessary dependency for FedOD

Reproducibility: Run demo_{xxx}.py to see the performance results of a specific OD algorithms. Note that:

• you may run multiple times to get the average performance
• we are cleaning up and releasing more reproducible experiments