Tech Glossary
Federated Learning
Federated learning is a decentralized machine learning approach where data remains distributed across multiple devices or servers, and models are trained locally on each device. Rather than centralizing data in one location, federated learning allows individual devices (such as smartphones, IoT devices, or edge servers) to collaboratively train a shared machine learning model without sending raw data to a central server. This paradigm enhances privacy, security, and efficiency, particularly in scenarios where sensitive or large datasets are involved.
The typical workflow of federated learning involves several steps:
A central server sends an initial global model to participating devices.
Each device trains the model locally using its own dataset, generating updated model parameters.
The devices send their locally updated parameters back to the central server, without sharing any raw data.
The central server aggregates these parameters (e.g., using techniques like averaging) to update the global model.
The process repeats, with the global model gradually improving over multiple iterations.
Federated learning is particularly useful in scenarios where data privacy is a primary concern. For example, in healthcare, sensitive patient data is stored across multiple hospitals or devices, and sharing this data could violate privacy regulations like GDPR or HIPAA. Federated learning enables hospitals to collaboratively train models for tasks such as disease prediction without exposing raw patient data.
Another significant use case is in mobile devices. Federated learning has been employed by companies like Google to improve personalized services (e.g., predictive text or voice recognition) across millions of smartphones. Each device contributes to model improvement based on its usage patterns, while keeping user data secure on the device.
In conclusion, federated learning represents a shift towards more secure, privacy-conscious machine learning by decentralizing the training process. It allows organizations and devices to build powerful, collaborative models without compromising sensitive data, making it a valuable approach in industries like healthcare, finance, and mobile services.