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System Architecture Overview

The Federated Learning Platform (FLIP) is designed as a distributed, microservices-based system that enables secure federated learning across multiple devices while maintaining data privacy and providing comprehensive monitoring capabilities.

High-Level Architecture

graph TB
    subgraph "Client Tier"
        WEB[Web Browser]
        MOBILE[Mobile Apps]
        API_CLIENT[API Clients]
    end

    subgraph "Presentation Layer"
        LB[Load Balancer]
        NGINX[Nginx Reverse Proxy]
        UI[Next.js Frontend<br/>React + TypeScript]
    end

    subgraph "Application Layer"
        API[FastAPI Backend<br/>Python 3.10]
        AUTH[Authentication Service]
        WS[WebSocket Service]
        UPLOAD[File Upload Service]
    end

    subgraph "Federated Learning Layer"
        SUPERLINK[Flower Superlink<br/>Communication Hub]
        AGGREGATOR[FL Aggregator<br/>Model Coordination]

        subgraph "Distributed Nodes"
            SN1[Supernode 1<br/>Partition 0]
            SN2[Supernode 2<br/>Partition 1]
            CLIENT1[Client App 1]
            CLIENT2[Client App 2]
            CLIENTN[Client App N]
        end
    end

    subgraph "Data Layer"
        MONGO[(MongoDB<br/>Primary Database)]
        REDIS[(Redis Cache<br/>Session Store)]
        FS[File System<br/>Model Storage]
    end

    subgraph "Observability Layer"
        OTEL[OpenTelemetry<br/>Collector]
        TEMPO[Tempo<br/>Tracing Backend]
        GRAFANA[Grafana<br/>Visualization]
        PROMETHEUS[Prometheus<br/>Metrics]
    end

    subgraph "Infrastructure Layer"
        DOCKER[Docker Containers]
        ANSIBLE[Ansible Automation]
        NETWORK[Docker Networks]
    end

    %% Client connections
    WEB --> LB
    MOBILE --> LB
    API_CLIENT --> LB

    %% Load balancing
    LB --> NGINX
    NGINX --> UI
    NGINX --> API

    %% Application layer connections
    UI --> API
    API --> AUTH
    API --> WS
    API --> UPLOAD

    %% Federated learning connections
    API --> SUPERLINK
    SUPERLINK --> AGGREGATOR
    AGGREGATOR --> SN1
    AGGREGATOR --> SN2
    SN1 --> CLIENT1
    SN1 --> CLIENT2
    SN2 --> CLIENTN

    %% Data layer connections
    API --> MONGO
    AUTH --> REDIS
    AGGREGATOR --> FS

    %% Observability connections
    API --> OTEL
    AGGREGATOR --> OTEL
    CLIENT1 --> OTEL
    CLIENT2 --> OTEL
    CLIENTN --> OTEL
    OTEL --> TEMPO
    OTEL --> PROMETHEUS
    TEMPO --> GRAFANA
    PROMETHEUS --> GRAFANA

    %% Infrastructure
    DOCKER --> NETWORK
    ANSIBLE --> DOCKER

Architectural Principles

1. Microservices Architecture

The system is decomposed into loosely coupled services, each responsible for specific business capabilities:

  • Frontend Service: User interface and client-side logic
  • Backend API: Core business logic and orchestration
  • Authentication Service: User management and security
  • Federated Learning Services: ML training coordination
  • Observability Services: Monitoring and telemetry

2. Event-Driven Communication

Services communicate through: - REST APIs: Synchronous request-response patterns - WebSockets: Real-time bidirectional communication - gRPC: High-performance federated learning communication - Message Queues: Asynchronous event processing (future enhancement)

3. Data Privacy by Design

  • Federated Learning: Data never leaves client devices
  • Secure Aggregation: Only model updates are shared
  • Encryption: All communication encrypted in transit
  • Access Control: Role-based access control (RBAC)

4. Scalability and Resilience

  • Horizontal Scaling: Support for multiple client devices
  • Container Orchestration: Docker-based deployment
  • Health Monitoring: Comprehensive health checks
  • Graceful Degradation: System continues operating with partial failures

Component Interaction Flow

sequenceDiagram
    participant User
    participant Frontend
    participant Backend
    participant Auth
    participant Superlink
    participant Aggregator
    participant Client
    participant Database
    participant Monitoring

    User->>Frontend: Access Application
    Frontend->>Backend: API Request
    Backend->>Auth: Validate Token
    Auth-->>Backend: Token Valid
    Backend->>Database: Query Data
    Database-->>Backend: Return Data
    Backend-->>Frontend: API Response
    Frontend-->>User: Display UI

    User->>Frontend: Start FL Training
    Frontend->>Backend: Training Request
    Backend->>Superlink: Initialize Training
    Superlink->>Aggregator: Setup Aggregation
    Aggregator->>Client: Training Instructions
    Client->>Client: Local Training
    Client->>Aggregator: Model Updates
    Aggregator->>Aggregator: Aggregate Models
    Aggregator->>Backend: Training Status
    Backend->>Monitoring: Log Metrics
    Backend-->>Frontend: Status Update
    Frontend-->>User: Training Progress

Deployment Architecture

Development Environment

graph LR
    subgraph "Developer Machine"
        DEV[Development<br/>Environment]
        DOCKER_DEV[Docker Compose<br/>Local Services]
        CODE[Source Code<br/>Hot Reload]
    end

    DEV --> DOCKER_DEV
    CODE --> DEV

Production Environment

graph TB
    subgraph "Orchestrator Node"
        ORCH[Orchestrator<br/>Services]
        FRONTEND[Frontend<br/>Container]
        BACKEND[Backend<br/>Container]
        MONITOR[Monitoring<br/>Stack]
    end

    subgraph "Aggregator Node"
        AGG_CONTAINER[Aggregator<br/>Container]
        SUPERLINK_CONTAINER[Superlink<br/>Container]
    end

    subgraph "Client Nodes"
        CLIENT_NODE1[Client Node 1<br/>Container]
        CLIENT_NODE2[Client Node 2<br/>Container]
        CLIENT_NODEN[Client Node N<br/>Container]
    end

    subgraph "Infrastructure"
        ANSIBLE_CONTROL[Ansible<br/>Control Node]
        SSH[SSH<br/>Connections]
    end

    ANSIBLE_CONTROL --> SSH
    SSH --> ORCH
    SSH --> AGG_CONTAINER
    SSH --> CLIENT_NODE1
    SSH --> CLIENT_NODE2
    SSH --> CLIENT_NODEN

    ORCH --> AGG_CONTAINER
    AGG_CONTAINER --> CLIENT_NODE1
    AGG_CONTAINER --> CLIENT_NODE2
    AGG_CONTAINER --> CLIENT_NODEN

Network Architecture

Port Allocation

Service Port Protocol Purpose
Frontend 4000 HTTP Web UI
Backend API 8000 HTTP REST API
MongoDB 27017 TCP Database
Superlink 9091, 9093 gRPC FL Communication
Aggregator 9092 gRPC Model Aggregation
OpenTelemetry 4317, 4318 gRPC/HTTP Telemetry
Tempo 3200 HTTP Tracing UI
Grafana 3001 HTTP Monitoring UI
Client Inference 8082+ HTTP Model Inference

Network Segmentation

graph TB
    subgraph "Public Network"
        INTERNET[Internet]
        CDN[CDN/Load Balancer]
    end

    subgraph "DMZ Network"
        REVERSE_PROXY[Reverse Proxy<br/>Nginx]
        FRONTEND_NET[Frontend Network<br/>10.0.1.0/24]
    end

    subgraph "Application Network"
        APP_NET[Application Network<br/>10.0.2.0/24]
        API_SERVICES[API Services]
        AUTH_SERVICES[Auth Services]
    end

    subgraph "Data Network"
        DATA_NET[Data Network<br/>10.0.3.0/24]
        DATABASE[Database Services]
        STORAGE[File Storage]
    end

    subgraph "FL Network"
        FL_NET[FL Network<br/>10.0.4.0/24]
        FL_SERVICES[FL Services]
        CLIENT_NODES[Client Nodes]
    end

    subgraph "Monitoring Network"
        MON_NET[Monitoring Network<br/>10.0.5.0/24]
        OBSERVABILITY[Observability Stack]
    end

    INTERNET --> CDN
    CDN --> REVERSE_PROXY
    REVERSE_PROXY --> FRONTEND_NET
    FRONTEND_NET --> APP_NET
    APP_NET --> DATA_NET
    APP_NET --> FL_NET
    APP_NET --> MON_NET
    FL_NET --> CLIENT_NODES

Security Architecture

Authentication & Authorization Flow

sequenceDiagram
    participant Client
    participant Frontend
    participant Backend
    participant AuthService
    participant Database

    Client->>Frontend: Login Request
    Frontend->>Backend: POST /auth/login
    Backend->>AuthService: Validate Credentials
    AuthService->>Database: Query User
    Database-->>AuthService: User Data
    AuthService->>AuthService: Generate JWT
    AuthService-->>Backend: JWT Token
    Backend-->>Frontend: Token + User Info
    Frontend->>Frontend: Store Token
    Frontend-->>Client: Login Success

    Note over Client,Database: Subsequent Requests

    Client->>Frontend: API Request
    Frontend->>Backend: Request + JWT Header
    Backend->>AuthService: Validate JWT
    AuthService-->>Backend: Token Valid + Claims
    Backend->>Backend: Check Permissions
    Backend-->>Frontend: API Response
    Frontend-->>Client: Data/UI Update

Data Flow Security

  • TLS/SSL: All external communication encrypted
  • JWT Tokens: Stateless authentication with expiration
  • RBAC: Role-based access control for resources
  • Input Validation: Comprehensive input sanitization
  • Rate Limiting: API rate limiting and DDoS protection

Performance Considerations

Scalability Patterns

  1. Horizontal Scaling: Add more client nodes for increased capacity
  2. Load Balancing: Distribute requests across multiple instances
  3. Caching: Redis for session management and frequently accessed data
  4. Database Optimization: MongoDB indexing and connection pooling
  5. CDN Integration: Static asset delivery optimization

Performance Metrics

  • Response Time: API response times < 200ms (95th percentile)
  • Throughput: Support for 100+ concurrent federated learning clients
  • Availability: 99.9% uptime target
  • Resource Utilization: CPU < 80%, Memory < 85%

Technology Decisions

Framework Selection Rationale

Component Technology Rationale
Frontend Next.js + TypeScript SSR, type safety, React ecosystem
Backend FastAPI + Python High performance, async support, ML ecosystem
Database MongoDB Document flexibility, horizontal scaling
FL Framework Flower (Flwr) Production-ready, gRPC-based, active community
Containerization Docker Consistency, portability, ecosystem
Orchestration Ansible Agentless, declarative, SSH-based
Monitoring OpenTelemetry + Grafana Vendor-neutral, comprehensive observability

Trade-offs and Alternatives

Chosen: FastAPI vs Django - ✅ Better async performance for ML workloads - ✅ Automatic API documentation - ❌ Less mature ecosystem than Django

Chosen: MongoDB vs PostgreSQL - ✅ Better fit for document-based ML configurations - ✅ Easier horizontal scaling - ❌ Less ACID guarantees than PostgreSQL

Chosen: Ansible vs Kubernetes - ✅ Simpler for edge device deployment - ✅ SSH-based, no agent required - ❌ Less sophisticated orchestration than K8s

Future Architecture Enhancements

Planned Improvements

  1. Message Queue Integration: Redis/RabbitMQ for async processing
  2. API Gateway: Centralized API management and rate limiting
  3. Service Mesh: Istio for advanced traffic management
  4. Multi-Region Support: Geographic distribution capabilities
  5. Advanced ML Pipeline: MLOps integration with model versioning

Scalability Roadmap

  • Phase 1: Current architecture (up to 100 clients)
  • Phase 2: Kubernetes migration (up to 1000 clients)
  • Phase 3: Multi-region deployment (global scale)
  • Phase 4: Edge computing integration (IoT devices)

Next: Continue to Component Architecture for detailed component specifications.