Anomaly Detection for Cyber Security using Federated Learning by Bargava Subramanian скачать в хорошем качестве

Anomaly Detection for Cyber Security using Federated Learning by Bargava Subramanian

In a network of connected devices, there are two critical aspects of the system to succeed: Security - with a number of internet-connected devices, securing the network from cyber threats is very important. Privacy - The devices capture business sensitive data that the Organisation has to safeguard to maintain their differentiation. I've used Federated learning to build anomaly detection models that monitor data quality and cybersecurity - while preserving data privacy. Federated learning enables Edge devices to collaboratively learn deep learning models 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 me the following advantages: Ability to build more accurate models faster Low latency during inference Privacy-preserving Improved energy efficiency of the devices I built deep learning models using tensorflow and deployed using uTensor. uTensor is a light-weight ML inference framework built on Mbed and Tensorflow.In this talk, I will discuss in detail on how I built federated learning models on the edge devices. More details: https://confengine.com/odsc-india-201... Conference Link: https://india.odsc.com