Learning on the edge refers to a machine learning approach in which data analysis and model training occur on local devices, such as smartphones, sensors, or IoT devices, rather than being sent to a centralized cloud server. This approach allows for real-time analysis and decision-making, as data can be processed and acted upon immediately, without requiring a connection to the cloud. Learning on the edge can also reduce latency, bandwidth, and security concerns associated with transmitting large amounts of data to the cloud. However, learning on the edge requires efficient algorithms and hardware that can handle data processing and model training on resource-constrained devices.