  When dealing with the Internet of Things (IoT), especially industrial IoT
(IIoT), two manifest challenges leap to mind. First is the massive amount of
data streaming to and from IoT devices, and second is the fast pace at which
these systems must operate. Distributed computing in the form of edge/cloud
structure is a popular technique to overcome these two challenges. In this
paper, we propose ADDAI (Anomaly Detection using Distributed AI) that can
easily span out geographically to cover a large number of IoT sources. Due to
its distributed nature, it guarantees critical IIoT requirements such as high
speed, robustness against a single point of failure, low communication
overhead, privacy, and scalability. Through empirical proof, we show the
communication cost is minimized, and the performance improves significantly
while maintaining the privacy of raw data at the local layer. ADDAI provides
predictions for new random samples with an average success rate of 98.4% while
reducing the communication overhead by half compared with the traditional
technique of offloading all the raw sensor data to the cloud.
