Federated Learning is a machine learning paradigm that allows for training machine learning models across multiple decentralized devices while keeping the data on the devices. This tutorial will introduce you to the basics of Federated Learning, its benefits, and how it works.
What is Federated Learning?
Federated Learning is a decentralized machine learning approach that enables multiple devices to collaboratively train a shared model while keeping their data local. This is particularly useful in scenarios where data privacy is a concern, such as in healthcare or finance.
Key Benefits
- Privacy: Data remains on the device, reducing the risk of data breaches.
- Scalability: Can be applied to large-scale deployments across various devices.
- Security: Reduces the need for data transmission, minimizing the risk of data interception.
How Does it Work?
Federated Learning works by following these steps:
- Initialization: A central server initializes a model.
- Training: Devices download the model and train on their local data.
- Update: Devices send their model updates back to the server.
- Aggregation: The server aggregates the updates and updates the global model.
- Communication: The updated model is sent back to the devices for further training.
Getting Started
To get started with Federated Learning, you can refer to our Federated Learning Setup Guide.
Prerequisites
- Basic understanding of machine learning and deep learning.
- Familiarity with Python and TensorFlow or PyTorch.
Resources
Federated Learning Architecture