Advanced Features Everything you need for medical federated learning

MediNet integrates modern technologies into a complete and intuitive platform, designed specifically for healthcare professionals.

Core Features

Essential tools for medical AI collaboration

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Hospital Project Management

Organize and manage multiple medical research projects with an intuitive interface that enables collaboration between different healthcare institutions.

  • check_circle Visual organization with color codes
  • check_circle Access control by user and role
  • check_circle Granular permission management
  • check_circle Complete activity history
Project management view

Project management interface with color-organized card view

connect_without_contact

Secure Federated Connections

Establish secure connections between hospitals and medical centers to collaborate on federated learning projects without compromising data privacy.

  • check_circle Secure connection with automatic validation
  • check_circle Real-time status monitoring
  • check_circle End-to-end communication encryption
  • check_circle Centralized credential management
Connections management view

Connections panel showing connectivity status with multiple hospitals

dataset

Intelligent Dataset Management

Visualize, analyze and select medical datasets from multiple institutions with advanced data analysis tools and secure preview capabilities.

  • check_circle Preview without data transfer
  • check_circle Automatic descriptive statistics
  • check_circle Dataset compatibility validation
  • check_circle Data quality analysis
Dataset analysis view

Statistical dataset analysis with distribution charts and quality metrics

settings

Visual Model Configuration

Configure neural network architectures with flexible parameter settings and predefined templates optimized for medical applications.

  • check_circle Template-based model creation
  • check_circle Predefined architectures for medical cases
  • check_circle Parameter validation and optimization
  • check_circle Medical domain-specific configurations
Model configuration interface

Configuration interface showing model parameter settings and templates

model_training

Federated Training

Federated training system integrated with Flower framework for federated learning and orchestraction.

  • check_circle Integration with Flower (flwr) framework
  • check_circle Multiple client configuration
  • check_circle Automatic federated round management
Federated training progress view

Federated training monitor showing real-time progress of multiple clients

dashboard

Real-time Monitoring

Interactive dashboard with advanced visualizations that allow you to follow training progress, performance metrics and status of each participating client.

  • check_circle Interactive charts with Chart.js
  • check_circle Real-time convergence metrics
  • check_circle Individual status of each client
  • check_circle Automatic alerts for important events
Real-time monitoring dashboard

Main dashboard with training metrics, convergence charts and client status

Technical Features

Modern technology for healthcare professionals

api

Complete REST API

Complete REST API with detailed documentation for custom integrations and process automation.

JSON API Rate Limiting Authentication
security

Advanced Security

Implementation of the highest security standards to protect sensitive medical data and comply with regulations.

GDPR HIPAA E2E Encryption
speed

Optimized Performance

Optimized architecture to efficiently handle large data volumes and multiple simultaneous training sessions.

Async Processing Caching Load Balancing
notifications

Notification System

Complete real-time notification system to keep you informed about the status of your training sessions.

Real-time Email In-app
analytics

Analysis and Comparison

Advanced analysis tools to compare models, evaluate performance and generate detailed reports.

ML Metrics Visualizations Reports
cloud

Cloud Scalability

Designed to scale from small pilot studies to large multi-institutional consortiums.

Docker Kubernetes Auto-scaling
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Implementation Status

Current development state of platform features and components

check_circle Implemented Feature is fully developed and tested
schedule In Progress Currently under development
event Planned Scheduled for future development

Platform Features Status

Platform Feature
Description
Status
Target Date
dashboard Web Dashboard
Main user interface and controls
check_circle Implemented
Completed
psychology Federated Learning Core
Basic FL training and aggregation
check_circle Implemented
Completed
lock Authentication System
User login and session management
check_circle Implemented
Completed
connect_without_contact Federated Connections
Secure hospital-to-hospital connections
check_circle Implemented
Completed
api REST API
Complete API for integrations
check_circle Implemented
Completed
model_training Model Studio
Visual model design and templates
schedule In Progress
Q3 2025
storage Dataset Management
Data upload, validation, and management
check_circle Implemented
Completed
notifications Real-time Notifications
System alerts and updates
event Planned
Q4 2025
analytics Advanced Analytics
Performance metrics and insights
schedule In progress
Q4 2025
architecture Drag & Drop Model Designer
Visual drag-and-drop model creation
event_note Planned
Q2 2026
functions Federated Support Vector Machines
Distributed SVM training with privacy-preserving hyperplane optimization
event_note Planned
Q4 2025
account_tree Federated Random Forest
Ensemble learning with distributed decision tree aggregation
event_note Planned
Q4 2025

Ready to Experience MediNet?

Explore how these features can support your work with medical AI

play_arrow User Guide medical_services Real Use Cases