Embedded Files
~2024
1. Federated Learning
FedOps Overview
- FedOps was designed and implemented to enable FL lifecycle operations management by extending the existing MLOps concept.
- FedOps is to manage the entire FL process of creating a global model by deploying and training the local model created in the experimental environment.
FedOps Detail
1. Easily application of data and models to the FL environment (FL Scalize)
2. Continuously check and manage FL Client/Server state (Manager)
3. Contribution Evaluation and Client Selection to give Incentive to Client (CE/CS & BCFL)
4. Easily deploy FL Client/Server and perform periodic FL rounds (CI/CD/CFL)
5. FL Client/Server monitoring dashboard for FL Lifecycle Visualization (Monitoring)
FedOps Demo
- Kubernetes-based Simulation
Client CI/CD
Deploying and running client pods using ArgoCD
Client CI/CD
Deploying and running client pods using ArgoCD
Creating FL tasks & CFL
Creating FL tasks to support a workflow using Airflow that creates an FL round using Airflow
Creating FL tasks & CFL
Creating FL tasks to support a workflow using Airflow that creates an FL round using Airflow
FL Lifecycle Monitoring
Performance and time cost monitoring of global model and local models using ELK
FL Lifecycle Monitoring
Performance and time cost monitoring of global model and local models using ELK
- Distributed Real Device
Docker-based FL Deployment to Real Devices
Monitoring the health of data on your device and the performance and time cost of local model using ELK
Docker-based FL Deployment to Real Devices
Monitoring the health of data on your device and the performance and time cost of local model using ELK
Ongoing National Project (2022.7 ~ 2024.12)
: Ministry of Science and Technology Employment Technology Promotion Agency
- End Goal
Blockchain Convergence Federated Learning Remote Clinical Trial Service for Digital Healthcare
- Utilization Plan
“Blockchain Convergence Federated Learning” developed through this project is used as a core technology for remote clinical trials targeting Digital Therapeutics(DTx), Digital Patient Monitoring, and Software as Medical Device(SaMD)
- Participating Institutions
2. TAVR Medical Twin based on RWD
Post-TAVR Complications
The FL Post-TAVR Complication Prediction System
3. Sensor-based Behavior Recognition and Risk Detection
Real-Time Risk Prediction for Behavioral Management in Psychiatric Wards
Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders
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