Distributed Learning

Distributed learning is a method where a large AI model is trained across multiple computers or servers instead of a single one. Each machine processes a part of the data and learns locally. Through communication, they share their findings to collaboratively improve the overall model. This approach allows handling massive datasets efficiently, speeds up training, and enhances privacy since sensitive data can stay on local devices. It's like a team of experts working together remotely, sharing insights to build a more accurate and robust system.