AI Federated Learning

AI Federated Learning is a distributed machine learning approach where multiple devices or organizations collaboratively train a single AI model without ever sharing their raw, private data with a central server. Instead of moving data to the model, the model is sent to the data, where local models are trained and only their updates are shared and combined by a central server to form an improved global model, ensuring data privacy and security. 

How it Works

1. Local Training: Each participating device or server trains a local AI model using its own private data. 
2.  Model Update Sharing: Only the parameters or updates of the local models are shared with a central server, not the actual data. 
3.  Global Model Aggregation: The central server aggregates these model updates to create an improved, global AI model. 
4.  Model Redistribution: The updated global model is then sent back to the devices for further local training. 
5.  Iterative Process: This cycle repeats until the AI model reaches desired performance goals. 

Key Benefits

• Data Privacy: Raw data stays on the user’s device, enhancing privacy and security, especially for sensitive information. 
• Data Security: Model updates, not the data itself, are shared, reducing the risk of data breaches. 
• Access to Diverse Data: Allows training on large, diverse, and decentralized datasets that would be difficult to centralize due to legal, logistical, or privacy concerns. 
• Reduced Data Transfer: Minimizes the amount of raw data that needs to be transferred to a central location. 

Applications

• Healthcare: Training AI models to diagnose diseases or analyze medical images without accessing patient records. 
• Mobile Devices: Improving features like next-word prediction on smartphones without sending user typing data to the cloud. 
• Finance: Training fraud detection models across different banks without them sharing customer transaction data.