Sedna v0.1.0 release

Incremental Learning

• Support Automatically retraining, evaluating, and updating models based on the data generated at the edge.
• Support time trigger, sample size trigger, and precision-based trigger.
• Support hard sample discovering of unlabeled data, for reducing the manual labeling workload.

Federated Learning

• Support automatic deployment of federated learning training scripts to the edge.
• Support user-defined aggregation algorithms.
• Integrate the FedAvg algorithm.

Joint Inference

• Supports automatic deployment of big model and little model to cloud and edge.
• Supports discovering hard examples and sending them to the cloud to improve the overall inference accuracy.

Published images

The published images can be found under docker.io/kubeedge:
kubeedge/sedna-gm:v0.1.0
kubeedge/sedna-lc:v0.1.0
kubeedge/sedna-example-joint-inference-helmet-detection-big:v0.1.0
kubeedge/sedna-example-joint-inference-helmet-detection-little:v0.1.0
kubeedge/sedna-example-incremental-learning-helmet-detection:v0.1.0
kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.1.0
kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.1.0