Federated Learning is a machine learning technique that enables multiple parties to collaboratively train a model without sharing their raw data. Instead, each party trains a local model on its own data and shares only the model updates with a central server. The central server aggregates these updates to improve the global model, which is then distributed back to the parties for further training. This approach ensures data privacy and security while still allowing for collaborative model training.
Example: Consider a scenario where multiple hospitals want to develop a machine learning model to predict patient outcomes based on medical records. Instead of sharing sensitive patient data with each other, each hospital can train a local model on its own data. The hospitals then share only the updates (e.g., weights and biases) of their local models with a central server. The server aggregates these updates to create a global model that is more accurate and robust than any individual hospital's model. This global model is then sent back to the hospitals for further training, improving its performance over time.
Related Service: Tencent Cloud offers a Federated Learning platform called "WeBank AI Platform - Federated Learning". This platform provides a secure and efficient environment for multiple parties to collaboratively train machine learning models while keeping their data private. It supports various machine learning frameworks and provides tools for data preprocessing, model training, and evaluation.