Edge AI Approaches: Trained-in-Cloud VS Localized Knowledge Distillation
When it comes to edge AI, two distinct approaches have emerged: Trained-in-Cloud and Localized Knowledge Distillation. These methods address the challenge of deploying AI models in resource-constrained edge devices, such as smart home devices, autonomous vehicles, and IoT sensors.
Trained-in-Cloud
Trained-in-Cloud leverages massive cloud infrastructure for model training, taking advantage of scalability, compute power, and storage. This approach involves uploading large datasets to the cloud, training the model, and then deploying the pre-trained model to edge devices. Once deployed, the edge device can fine-tune the model using local data, adapting to its specific environment.
However, Trained-in-Cloud has its limitations. It requires a stable internet connection, which may not always be available in edge environments. Moreover, transmitting sensitive data to the cloud raises privacy concerns.
**Local...
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