Federated Learning
A machine learning approach where models are trained across multiple devices or organizations without sharing the underlying data, improving privacy and security.
What is Federated Learning?
In federated learning, participating devices or systems train a shared model using their local data. Instead of sending raw data to a central server, they send model updates that are combined to improve the global model. This approach allows organizations to collaborate on model training while keeping sensitive or distributed datasets closer to their original locations.
Why is Federated Learning Important?
Centralizing sensitive data can create privacy, security, and regulatory challenges. Federated learning can reduce the need to transfer raw data while still allowing models to learn from multiple sources. However, additional safeguards may still be required to protect model updates and prevent information leakage.
Common use cases
Federated learning is commonly used in healthcare, mobile devices, financial services, IoT systems, privacy-preserving AI, and cross-organization machine learning.