Federated learning is a decentralized ML approach where models are trained across multiple devices or servers holding local data samples, without exchanging the data itself. This preserves privacy while enabling collaborative learning.

NYD Application: Could enable our team to collaboratively improve AI models across different client projects without sharing sensitive data.

Example: "Using federated learning, we improved our design recommendation model using data from multiple client sites without compromising privacy."