Skip to content

FUTURE.md

Latest commit

 

History

History
149 lines (113 loc) · 8.65 KB

FUTURE.md

File metadata and controls

149 lines (113 loc) · 8.65 KB

FLOPY-NET Future Roadmap

This document outlines the planned future work and potential improvements for the FLOPY-NET platform based on the current v1.0.0-alpha.8 implementation status.

Current State Assessment

FLOPY-NET v1.0.0-alpha.8 provides a solid foundation with:

  • Flower Framework Integration: Full Flower-based FL server and client implementation
  • Container Architecture: Docker Compose with static IP assignment (192.168.100.0/24)
  • Core Services: Policy Engine, Collector, SDN Controller, OpenVSwitch all operational
  • GNS3 Integration: Functional network simulation with programmable topology control
  • Dashboard System: React frontend with FastAPI backend for real-time monitoring
  • Model Support: PyTorch-based CNN/MLP models for MNIST, CIFAR-10, and financial datasets

Prioritized Next Steps (Immediate Focus)

Based on the current implementation, the following high-impact features should be prioritized:

  1. Enhanced Real Dataset Training: Expand the current model implementations to support more realistic datasets and improve training performance with the existing PyTorch + Flower integration.

  2. Advanced Network Scenario Library: Leverage the existing GNS3 and SDN integration to create a comprehensive library of network scenarios with pre-configured topologies for common FL research patterns.

  3. Policy Engine Enhancement: Extend the current Flask-based Policy Engine with more sophisticated policy types, including dynamic client selection, security policies, and automated network response policies.

  4. Dashboard Interactivity: Enhance the React dashboard to provide interactive control over the GNS3 topology, allowing researchers to modify network conditions in real-time during experiments.

  5. Metrics Analysis Tools: Build upon the existing SQLite-based metrics collection to provide automated analysis, comparison tools, and research-ready data export capabilities.

Thematic Roadmap Details

The following sections provide a detailed breakdown of planned features based on the current implementation.

1. Enhanced Federated Learning Capabilities

Building upon the existing Flower framework integration and PyTorch model support.

1.1. Advanced Model Architectures

  • Current State: Basic CNN and MLP models for MNIST/CIFAR-10
  • Next Step: Expand model support for state-of-the-art architectures
    • Action: Implement ResNet, VGG, and Transformer architectures within the existing model handler system
    • Action: Add support for natural language processing models using Hugging Face integration
    • Action: Create scenario-specific models for computer vision, NLP, and time-series forecasting

1.2. Advanced Aggregation Algorithms

  • Current State: FedAvg implementation through Flower framework
  • Next Step: Implement robust aggregation methods to handle non-IID data and Byzantine clients
    • Action: Extend the FL Server with FedProx, FedNova, and SCAFFOLD algorithms
    • Action: Add Byzantine-robust aggregation (Krum, Trimmed Mean, Byzantine-robust FedAvg)
    • Action: Implement adaptive aggregation based on network conditions and client reliability

1.3. Privacy-Preserving Mechanisms

  • Current State: Basic FL client with data locality
  • Next Step: Implement advanced privacy protection mechanisms
    • Action: Integrate differential privacy using PyTorch Opacus within FL clients
    • Action: Add secure aggregation protocols for parameter encryption
    • Action: Implement homomorphic encryption for sensitive data scenarios

2. Network Simulation Enhancement

Leveraging the existing GNS3 and SDN integration for more sophisticated network scenarios.

2.1. Advanced Network Scenario Library

  • Current State: Basic GNS3 integration with topology creation
  • Next Step: Create comprehensive pre-built network scenarios
    • Action: Develop scenario templates for edge computing, IoT networks, and cellular environments
    • Action: Implement hierarchical network topologies with multi-tier federated learning
    • Action: Add support for dynamic client join/leave scenarios during training

2.2. Enhanced SDN Control

  • Current State: Ryu controller with basic OpenFlow support
  • Next Step: Advanced programmable network behavior
    • Action: Implement QoS policies for differentiated FL traffic handling
    • Action: Add adaptive routing based on FL training progress and network conditions
    • Action: Develop network security policies that respond to detected anomalies

2.3. Real-time Network Condition Simulation

  • Current State: Static network condition configuration
  • Next Step: Dynamic network condition changes during experiments
    • Action: Implement time-based network condition scenarios (e.g., daily traffic patterns)
    • Action: Add event-driven network failures and recovery scenarios
    • Action: Create realistic mobility patterns for mobile federated learning scenarios

3. Enhanced Policy and Security Framework

Building upon the existing Flask-based Policy Engine.

3.1. Advanced Policy Types

  • Current State: Basic policy definitions and enforcement
  • Next Step: Sophisticated policy management and enforcement
    • Action: Implement client reputation systems with trust scoring
    • Action: Add dynamic client selection policies based on performance and reliability
    • Action: Create security policies that automatically respond to detected threats

3.2. Compliance and Audit Framework

  • Current State: Basic policy logging and event tracking
  • Next Step: Comprehensive compliance monitoring and reporting
    • Action: Implement regulatory compliance policies (GDPR, HIPAA)
    • Action: Add automated audit trail generation and reporting
    • Action: Create policy impact analysis and performance correlation tools

4. Dashboard and User Experience Improvements

Enhancing the existing React + FastAPI dashboard architecture.

4.1. Interactive Network Control

  • Current State: Read-only network topology visualization
  • Next Step: Interactive network topology management
    • Action: Add drag-and-drop network topology editor
    • Action: Implement real-time network condition adjustment controls
    • Action: Create scenario recording and playback functionality

4.2. Advanced Analytics and Visualization

  • Current State: Basic metrics visualization with charts
  • Next Step: Comprehensive research analytics platform
    • Action: Implement comparative analysis tools for multiple experiments
    • Action: Add statistical significance testing and visualization
    • Action: Create automated report generation for research publications

4.3. Experiment Management

  • Current State: Manual experiment configuration and execution with GNS3 template registration via scripts/gns3_templates.py
  • Next Step: Comprehensive experiment lifecycle management
    • Action: Implement experiment templating and versioning system
    • Action: Add collaborative features for multi-researcher environments
    • Action: Create experiment reproducibility verification tools
    • Action: Enhance the gns3_templates.py script with template versioning and automated custom image deployment

4.4. Enhanced Template and Image Management

  • Current State: Manual template registration using scripts/gns3_templates.py with basic register, list, clean, and update commands
  • Next Step: Automated template and custom image lifecycle management
    • Action: Implement automated custom image building and deployment workflows
    • Action: Add template versioning and rollback capabilities
    • Action: Create template dependency tracking and validation
    • Action: Develop automated testing for custom images before deployment to GNS3

4.5. Client Environment Context Reporting

  • Next Step: Provide context on the environment where each client is running.
    • Action: Modify the fl-client entrypoint script or application to collect system information (e.g., OS, CPU architecture, memory) using libraries like platform or psutil.
    • Action: Report this static information to the Collector upon startup.
    • Action: Display the host information in the dashboard's client list.

4.3. Enhanced CLI Tool & Documentation

  • Current State: The main.py script is a basic service launcher.
  • Next Step: Improve developer and user experience.
    • Action: Use a library like Click or Typer to build a richer CLI with commands like flopynet scenario run <name>, flopynet system status, flopynet policy list.
    • Action: Build a dedicated documentation website using MkDocs (already a dependency).