👉 Matched computing, also known as federated learning, is a distributed machine learning 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 dataset and sends only the model updates or gradients back to a central server, which aggregates these updates to refine the global model. This method enhances privacy and security, as sensitive data remains on the devices, and it addresses the challenges of data scarcity by leveraging diverse datasets across different locations. By doing so, matched computing enables the development of robust models that benefit from a broader range of data while minimizing the risk of data breaches or privacy violations.
