Federated Learning Platform - Developer Documentation¶

Welcome to the comprehensive developer documentation for the Federated Learning Platform (FLIP), a production-grade distributed machine learning system built with modern technologies and best practices.

Overview¶

This platform implements a complete federated learning solution that enables secure, distributed machine learning across multiple devices while preserving data privacy. The system combines a modern web interface, robust backend services, and advanced federated learning capabilities using the Flower framework.

Key Features¶

Distributed Training: Coordinate federated learning across multiple client devices
Web-Based Management: Intuitive Next.js frontend for system management
Ansible Automation: Complete infrastructure automation for multi-device deployment
Comprehensive Monitoring: OpenTelemetry, Tempo, and Grafana observability stack
Production-Ready: Docker containerization with scalable architecture
Security-First: Authentication, authorization, and secure communication
Real-Time Updates: WebSocket-based live monitoring and notifications

Architecture Overview¶

graph TB
    subgraph "Frontend Layer"
        UI[Next.js Frontend<br/>Port 4000]
    end

    subgraph "Backend Services"
        API[FastAPI Backend<br/>Port 8000]
        DB[(MongoDB<br/>Database)]
        WS[WebSocket<br/>Service]
    end

    subgraph "Federated Learning Core"
        SL[Superlink<br/>Port 9091/9093]
        AGG[Aggregator<br/>Port 9092]
        SN1[Supernode 1]
        SN2[Supernode 2]
        C1[Client 1]
        C2[Client 2]
        CN[Client N]
    end

    subgraph "Observability Stack"
        OTEL[OpenTelemetry<br/>Collector]
        TEMPO[Tempo<br/>Port 3200]
        GRAF[Grafana<br/>Port 3001]
    end

    subgraph "Infrastructure"
        DOCKER[Docker<br/>Containers]
        ANSIBLE[Ansible<br/>Automation]
    end

    UI --> API
    API --> DB
    API --> WS
    API --> SL
    SL --> AGG
    AGG --> SN1
    AGG --> SN2
    SN1 --> C1
    SN1 --> C2
    SN2 --> CN

    API --> OTEL
    AGG --> OTEL
    C1 --> OTEL
    C2 --> OTEL
    CN --> OTEL
    OTEL --> TEMPO
    TEMPO --> GRAF

    ANSIBLE --> DOCKER

Technology Stack¶

Frontend¶

Next.js 15 - React framework with server-side rendering
TypeScript - Type-safe JavaScript development
Tailwind CSS - Utility-first CSS framework
Chart.js - Data visualization and monitoring charts
Socket.IO - Real-time communication

Backend¶

FastAPI - High-performance Python web framework
MongoDB - Document-based database
WebSockets - Real-time bidirectional communication
JWT Authentication - Secure token-based authentication
Pydantic - Data validation and serialization

Federated Learning¶

Flower (Flwr) 1.15.2 - Federated learning framework
PyTorch - Deep learning framework
gRPC - High-performance RPC framework
Protocol Buffers - Efficient data serialization

Infrastructure & DevOps¶

Docker & Docker Compose - Containerization and orchestration
Ansible - Infrastructure automation and configuration management
OpenTelemetry - Observability and telemetry collection
Grafana Tempo - Distributed tracing backend
Grafana - Metrics visualization and dashboards

Quick Start¶

Prerequisites¶

Docker and Docker Compose
Python 3.10+
Node.js 18+
Git

Local Development Setup¶

Clone the repository
```
git clone <repository-url>
cd flip
```
Start the development environment
```
./setup-local-training.sh
```
Access the application
Frontend: http://localhost:4000
Backend API: http://localhost:8000
Grafana: http://localhost:3001 (admin/admin)

Production Deployment¶

For production deployment across multiple devices:

Setup SSH access to target devices
Configure Ansible inventory with device details

Run the orchestrator setup

./setup-and-run-orchestrator.sh production

Documentation Structure¶

This documentation is organized into the following sections:

🏗️ Architecture ¶

Comprehensive system architecture, component relationships, and design patterns.

🛠️ Technology Stack ¶

Detailed information about all technologies, frameworks, and dependencies.

💻 Development ¶

Development environment setup, workflows, and coding standards.

🔧 Backend ¶

FastAPI backend architecture, services, and API documentation.

🤖 Federated Learning ¶

Flower framework integration, training workflows, and distributed learning.

📊 Observability ¶

Monitoring, tracing, metrics collection, and dashboard configuration.

🚀 Deployment ¶

Docker setup, Ansible automation, and production deployment strategies.

🔒 Security ¶

Security considerations, authentication, and production best practices.

🔧 Operations ¶

Troubleshooting, maintenance, monitoring, and performance optimization.

📚 API Reference ¶

Complete API documentation with examples and usage patterns.

Production-Grade Recommendations¶

This platform is designed for production use with the following considerations:

Production Deployment

Before deploying to production, ensure you have reviewed the Security and Deployment sections for critical configuration requirements.

Key Production Features¶

Scalable Architecture: Horizontal scaling support for federated learning clients
Security Hardening: JWT authentication, secure communication, and data protection
Monitoring & Alerting: Comprehensive observability with Grafana dashboards
Automated Deployment: Ansible playbooks for consistent infrastructure management
Error Handling: Robust error handling and recovery mechanisms
Performance Optimization: Optimized for high-throughput federated learning workloads

Best Practices Implemented¶

Container Security: Multi-stage Docker builds with minimal attack surface
Configuration Management: Environment-based configuration with secrets management
Database Optimization: MongoDB with proper indexing and connection pooling
API Design: RESTful APIs with proper versioning and documentation
Code Quality: TypeScript for type safety, comprehensive testing coverage
Infrastructure as Code: Ansible automation for reproducible deployments

Getting Help¶

Issues & Bugs: Report issues in the project repository
Documentation: Browse this comprehensive documentation
API Reference: Check the API Reference section
Troubleshooting: See the Operations guide

Contributing¶

Please refer to the Contributing Guide for information on how to contribute to this project.

Next Steps: Start with the Architecture Overview to understand the system design, then proceed to the Development Setup guide to get your environment running.