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Signed-off-by: myan <[email protected]>

Author: yanmxa

programs/summerofcode/2025.md

@@ -406,15 +406,18 @@ as first-class [Meshery Models](https://docs.meshery.io/concepts/logical/models)
 - Expected Outcome:
   - Comprehensive Documentation:
     - Define the scenarios addressed by the prototype, highlighting its purpose and value.  
-    - Provide an intuitive and architectural comparison between **Federated Learning (FL)** and **OCM**, mapping FL terminology to OCM APIs to showcase OCM’s native support for FL.  
+    - Provide an intuitive and architectural comparison between **Federated Learning (FL)** and **OCM**, mapping FL terminology to OCM APIs to showcase OCM's native support for FL.  
     - Illustrate the complete **Federated Learning workflow** within **Open Cluster Management**.  
   - Extended Prototype (or CRD) Support:
     - Enable model aggregation persistence in **AWS S3** (currently supports only **native PVC**).  
-    - Extend compatibility to support additional **Federated Learning frameworks** like [OpenFL](https://github.com/securefederatedai/openfl) (currently supports **Flower**). This requires understanding how **OpenFL** works, containerizing it, and integrating it into the prototype.
+    - Extend compatibility to support additional **Federated Learning frameworks** like [OpenFL](https://github.com/securefederatedai/openfl) and [NVIDIA FLARE](https://developer.nvidia.com/flare) (currently supports **Flower**). This involves understanding how OpenFL, NVIDIA FLARE, and other frameworks function, containerizing them, and integrating them into the prototype.
+  - Enhanced Observability and Metrics for Federated Learning API:
+    - Define key metrics, expose them via API and ensure extensibility for future enhancements.  
+    - Track resource usage (optional), monitoring GPU, CPU, and memory usage for client training is beneficial but not essential.  
 
 - Recommended Skills: Golang, Kubernetes, Federated Learning, Open Cluster Management, Scheduling
 
-- Expected project size: medium (~175 hour projects)
+- Expected project size: large (~350 hour projects)
 
 - Mentor(s):
   - Meng Yan (@yanmxa, [email protected]) - primary