# On-Device Training (Edge AI Glossary)

## metadata
- term: On-Device Training
- slug: on-device-training
- letter: O
- route: /resources/glossary/on-device-training
- canonical: https://klyff.com/resources/glossary/on-device-training

## Definition & Context
On-device training is the process where AI models are trained and updated directly on edge devices (such as sensors, cameras, or IoT nodes) rather than in centralized cloud...

## Details
## On-Device Training

#### What is On-Device Training?

**On-device training** is the process where AI models are trained and updated **directly on edge devices**: such as sensors, cameras, or IoT nodes rather than in centralized cloud environments. This method enables **real-time learning** using local data, improving privacy, speed, and adaptability in Edge AI systems.

#### Why Is It Used?

On-device training eliminates dependency on cloud connectivity and ensures **data sovereignty** by keeping sensitive information local. It’s crucial for **latency-sensitive, privacy-first applications**, such as autonomous vehicles, smart manufacturing, or healthcare IoT, where decisions must adapt instantly to changing conditions.

#### How Is It Used?

AI models deployed on edge devices are first trained in the cloud, then **fine-tuned or retrained locally** using live data. Techniques like **federated learning**, **transfer learning**, and **model distillation** allow these models to improve continuously without centralized data aggregation.

#### Types of On-Device Training

 
- **Full On-Device Training:** Models learn entirely from scratch on local devices.
- **Incremental Learning:** Updates pre-trained models with new data samples.
- **Federated Learning:** Devices collaboratively train models while keeping data decentralized. 

#### Benefits of On-Device Training

 
- **Reduced Latency:** Real-time adaptability without cloud round trips.
- **Enhanced Privacy:** Data remains on-device, ensuring regulatory compliance.
- **Lower Bandwidth Costs:** Minimal data transmission to central servers.
- **Personalized Intelligence:** Models adapt to unique device or user behavior.
- **Resilience:** Works efficiently even in offline or low-connectivity environments.