There is an exponential growth in the number of Internet-enabled devices on modern smart buildings. In this talk, the speakers show how they built anomaly detection models using federated learning for a) data quality and b) cyber security. Federated learning is privacy-preserving and doesn’t require data to be moved to the cloud.
A modern smart building has a number of internet-enabled devices. IoT sensors to measure temperature, internet-enabled lighting, IP camera, IP Phone, etc. Data is generated at scale across all the devices. There are two critical aspects of the network of devices to function well:
But there are two broad challenges to achieve the above:
The speakers used Federated learning to build anomaly detection models that monitor data quality and cyber security - while preserving data privacy.
Federated learning enables Edge devices to collaboratively learn a machine learning model but keeping all of the data on the device itself. Instead of moving data to the cloud, the models are trained on the device and only the updates of the model are shared across the network. Using federated learning gave us the following advantages:
The speakers built deep learning models using pytorch and pysyft.
The speakers discuss their architecture and also show how federated learning can help improve the models. Federated learning provides a framework to port models across organizations for the same domain of the device. This is something that’s not possible in traditional cloud-based anomaly detection models. This makes it easy to deploy with very limited data and the speakers share some of their success stories.