Collaborative machine learning is a method where multiple devices or organizations work together to train a machine learning model without sharing their raw data. Instead of sending data to a central server, each participant trains the model locally and only shares the model updates. This approach helps maintain data privacy and security while still benefiting from collective knowledge. This technique is particularly useful in fields like healthcare and finance, where sensitive information must be protected. By collaborating, different entities can improve the model's accuracy and robustness, leading to better predictions and insights while adhering to data protection regulations.