# EdgeFLSim

**Repository Path**: wingter/EdgeFLSim

## Basic Information

- **Project Name**: EdgeFLSim
- **Description**: A simulator of edge-based Federated Learning system with simple UI
- **Primary Language**: Python
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-29
- **Last Updated**: 2026-04-29

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# EdgeFLSim
Modeling and Simulation Platform for Edge Intelligence Systems

## Project Introduction

This project is a modeling and simulation platform designed for **Edge Intelligence** and **Federated Learning**. The system supports fine-grained modeling of heterogeneous edge devices (CPU/GPU), Non-IID data partitioning, various device selection strategies, and global aggregation algorithms. It offers two deployment modes: single Base Station (BS) and multi-Base Station. The platform features real-time Web monitoring and interaction, allowing for dynamic parameter configuration, visualization of the training process, and export of experimental data (CSV, JSON).

## Key Features

- **Heterogeneous Device Modeling**: Supports mixed CPU/GPU architectures with dynamic fluctuations (±10%) in frequency, bandwidth, power, energy consumption coefficients.
- **Full Federated Learning Pipeline**: Local training → Upload → Aggregation → Distribution. Supports single BS (two-tier) and multi-BS (three-tier, edge server collaboration).
- **Device Selection Strategies**: Random, Energy-Aware, Capability-Aware, and Hybrid.
- **Global Aggregation Algorithms**: FedAvg, Energy-Weighted FedAvg, FedProx, q-FedAvg, and Capability-Weighted Averaging.
- **Data Heterogeneity Simulation**: Mixed IID / Non-IID partitioning with adjustable skew factors.
- **Plug-and-Play Models & Datasets**: Supports models such as LeNet5, SimpleCNN, and ResNet18; supports datasets including MNIST, Fashion-MNIST, EMNIST, CIFAR-10, CIFAR-100, and SVHN.
- **Real-time Web Monitoring**: Tracks accuracy, energy consumption, time, network traffic, and fairness curves; provides device status tables and detailed device-level metrics.
- **Data Export**: Automatically generates CSV files (training results, device energy consumption, device details, traffic history) and JSON configurations.

## Tech Stack

- **Backend**: Python 3.11, PyTorch 2.0, Flask, Flask-SocketIO, Eventlet
- **Frontend**: HTML5, Bootstrap 5, ECharts, Socket.IO
- **Deep Learning**: PyTorch, torchvision
- **Data Processing**: NumPy, Pandas, scikit-learn

## UI interface
![Alt text](EdgeFLSim_UI.png "A screenshot of EdgeFLSim's UI interface")

## System Architecture

> *For the detailed architecture diagram, please refer to the Mermaid description below or `docs/architecture.png` (rendered using [Mermaid Live Editor](https://mermaid.live/)).*

```mermaid
graph TB
    subgraph Frontend
        WebUI[Web Interface<br>Bootstrap+ECharts]
    end

    subgraph Backend
        Flask[Flask Server]
        SocketIO[Socket.IO Real-time Communication]
        Runner[Simulation Engine runner.py]
        Logger[Logging Module logger.py]
        Scheduler[Device Selection scheduler.py]
        Aggregator[Aggregation Algorithm server.py]
        EdgeServer[Edge Server edge_server.py]
        Device[Device Model device.py]
        ModelFactory[Model Factory model_factory.py]
        DataFactory[Dataset Factory dataset_factory.py]
    end

    subgraph Storage
        Results[(results/  CSV/JSON)]
        Data[(data/ Dataset Cache)]
    end

    WebUI -- HTTP/WebSocket --> Flask
    Flask --> Runner
    Runner --> Scheduler
    Runner --> Aggregator
    Runner --> Device
    Runner --> EdgeServer
    Runner --> Logger
    Logger --> Results
    Device --> ModelFactory
    Device --> DataFactory
    DataFactory --> Data
```

# EdgeFLSim
# 面向边缘智算系统的建模与仿真平台

## 项目简介

本项目是一个面向边缘智算（Edge Intelligence）与联邦学习（Federated Learning）的建模与仿真平台。系统支持异构边缘设备（CPU/GPU）的精细建模、非IID数据划分、多种设备选择策略和全局聚合算法，并提供单基站/多基站两种部署模式。平台具备实时Web监控与交互功能，可动态配置参数、可视化训练过程，并导出实验数据（CSV、JSON）。

## 主要特性

- **异构设备建模**：支持 CPU/GPU 混合，频率、带宽、功率、能耗系数动态波动（±10%）
- **联邦学习全流程**：本地训练 → 上传 → 聚合 → 下发，支持单基站（两级）和多基站（三级，边缘服务器协同）
- **多种设备选择策略**：随机、能量感知、能力感知、混合
- **多种全局聚合算法**：FedAvg、能量加权 FedAvg、FedProx、q‑FedAvg、算力加权平均
- **数据异构性模拟**：IID / Non‑IID 混合划分，偏斜因子可调
- **插拔式模型与数据集**：支持 LeNet5、SimpleCNN、ResNet18 等模型；支持 MNIST、Fashion‑MNIST、EMNIST、CIFAR‑10、CIFAR‑100、SVHN 等数据集
- **实时Web监控**：准确率、能耗、时间、网络流量、公平性曲线；设备状态表及设备级明细表格
- **实验数据导出**：自动生成 CSV 文件（训练结果、设备能耗、设备明细、流量历史）和 JSON 配置

## 技术栈

- **后端**：Python 3.11, PyTorch 2.0, Flask, Flask‑SocketIO, Eventlet
- **前端**：HTML5, Bootstrap 5, ECharts, Socket.IO
- **深度学习**：PyTorch, torchvision
- **数据处理**：NumPy, Pandas, scikit‑learn

## 系统架构

> 详细架构图见下方 Mermaid 描述或 `docs/architecture.png`（建议用 [Mermaid Live Editor](https://mermaid.live/) 渲染）。

```mermaid
graph TB
    subgraph 前端
        WebUI[Web界面<br>Bootstrap+ECharts]
    end

    subgraph 后端
        Flask[Flask 服务器]
        SocketIO[Socket.IO 实时通信]
        Runner[仿真引擎 runner.py]
        Logger[日志模块 logger.py]
        Scheduler[设备选择 scheduler.py]
        Aggregator[聚合算法 server.py]
        EdgeServer[边缘服务器 edge_server.py]
        Device[设备模型 device.py]
        ModelFactory[模型工厂 model_factory.py]
        DataFactory[数据集工厂 dataset_factory.py]
    end

    subgraph 存储
        Results[(results/  CSV/JSON)]
        Data[(data/ 数据集缓存)]
    end

    WebUI -- HTTP/WebSocket --> Flask
    Flask --> Runner
    Runner --> Scheduler
    Runner --> Aggregator
    Runner --> Device
    Runner --> EdgeServer
    Runner --> Logger
    Logger --> Results
    Device --> ModelFactory
    Device --> DataFactory
    DataFactory --> Data
```