Federated Inference
Running AI model inference across multiple distributed devices or locations, rather than centralizing it in one place. Each device processes its own data locally.
Why It Matters
Federated inference keeps sensitive data local while still providing AI capabilities, combining privacy with intelligence at the edge.
Example
Each hospital running a diagnostic AI model locally on their own patient scans, rather than sending images to a central cloud server for processing.
Think of it like...
Like each branch office having their own accountant rather than sending all financial documents to headquarters — the work happens locally where the data lives.
Related Terms
Federated Learning
A decentralized training approach where a model is trained across multiple devices or organizations without sharing raw data. Each participant trains locally and only shares model updates.
Edge Inference
Running AI models directly on local devices (phones, IoT sensors, cameras) rather than sending data to the cloud. This reduces latency, preserves privacy, and works without internet connectivity.
Inference
The process of using a trained model to make predictions on new, previously unseen data. Inference is what happens when an AI model is deployed and actively serving results to users.