 |
Traditional machine learning involves a data pipeline that uses a central server that hosts the trained model in order to make predictions.The downside of this architecture is that all the data collected by local devices and sensors are sent back to the central server for processing, and subsequently returned back to the devices. This round-trip limits a model¡¯s ability to learn in real-time.Federated learning (FL) in contrast, is an approach that downloads the current model and computes an updated model at the device itself (ala edge computing) using local data.
|
IEEE/ICACT20210192 Slide.03
[Big Slide]