  Anomaly detection is an important function in IoT applications for finding
outliers caused by abnormal events. Anomaly detection sometimes comes with
high-frequency data sampling which should be carried out at Edge devices rather
than Cloud. In this paper, we consider the case that multiple IoT devices are
installed in a single remote site and that they collaboratively detect
anomalies from the observations with device-to-device communications. For this,
we propose a fully distributed collaborative scheme for training distributed
anomaly detectors with Wireless Ad Hoc Federated Learning, namely
"WAFL-Autoencoder". We introduce the concept of Global Anomaly which sample is
not only rare to the local device but rare to all the devices in the target
domain. We also propose a distributed threshold-finding algorithm for Global
Anomaly detection. With our standard benchmark-based evaluation, we have
confirmed that our scheme trained anomaly detectors perfectly across the
devices. We have also confirmed that the devices collaboratively found
thresholds for Global Anomaly detection with low false positive rates while
achieving high true positive rates with few exceptions.
