An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices

Understand

Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines.

• However, retraining or customizing a model is required at edge devices as the model is becoming outdated due to environmental changes over time.
• To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model.
• In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device.