Federated Averaging is a technique used in federated learning, where multiple devices collaboratively train a machine learning model without sharing their raw data. Each device computes updates to the model based on its local data and then sends these updates to a central server. The server aggregates the updates by averaging them, creating a new model that reflects the collective knowledge of all participating devices. This process helps maintain data privacy while still improving the model's performance, making it suitable for applications like smartphones and IoT devices where sensitive information is involved.