Federated Learning 📱

Definition

Federated learning represents a paradigm shift in AI model training by allowing a centralized model to learn from decentralized data. Daniel Whitenack explains that instead of aggregating data in one place, federated learning enables training on data stored across multiple devices, maintaining privacy and reducing data transfer 1. This approach contrasts with traditional methods where data is centralized for model training. Chris Benson highlights resources like the Towards Data Science tutorial and Google's federated learning comic for those new to the concept 2.

   

Architecture

The architecture of federated learning involves a central server, often called a curator, that coordinates training across numerous client devices. Daniel Whitenack describes how this setup allows models to be trained on-device, using local data, and then updates are sent back to the server 3. This method ensures data privacy as only model updates, not raw data, are shared. Chris Benson notes the practical implementation of this architecture, emphasizing its current feasibility and the use of frameworks like Pygrid to manage training processes 4.