sedna
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kubeedge
Enhancements to sedna Federated Learning
The current Sedna Federated Learning API only defines two types of tasks: parameter aggregation in the cloud and model training at the edge, lacking model validation and deployment processes. This incomplete flow hinders the end-to-end lifecycle of AI models from development to deployment.
What needs to be done: This project aims to bridge the gap in the current Sedna Federated Learning tasks by implementing the complete validation and deployment process, enabling a closed-loop flow for AI models from production to deployment.
