👉 Commissioner computing, also known as federated learning or collaborative machine learning, is a distributed AI approach where multiple devices or nodes collaboratively train a shared model without exchanging their raw data. Instead, each device trains the model locally using its own data and only shares model updates or gradients with a central server, which aggregates these updates to improve the global model. This method enhances privacy and security by keeping sensitive data on the devices, reduces the need for large-scale data transfers, and can be more efficient in terms of bandwidth and computational resources. It's particularly useful in scenarios where data privacy is paramount, such as healthcare or finance, and where data cannot be easily centralized due to regulatory constraints.
