Federated Learning

Federated learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging data. Each device performs model training locally and only sends the model updates to a central server for aggregation, which optimizes the shared model. This method achieves privacy-preserving machine learning by ensuring that data remains stored locally and only the necessary information for model improvement is shared. The goal of federated learning is to enhance model performance while safeguarding user data security and privacy, making it highly valuable for a wide range of applications.