What is federated learning?

Federated learning is a method of training machine learning models on decentralized datasets, where the data is distributed across multiple devices, such as smartphones or computers, in a decentralized manner. This approach allows for training more accurate models by leveraging the data available on a large number of devices, without the need to centralize the data in a single location. This can be especially useful in situations where the data is sensitive and cannot be centrally collected and stored. In federated learning, the participating devices train a shared model by sending their local updates to a central server, which aggregates the updates to improve the global model. This process is repeated iteratively until the model has converged. [ChatGPT]