🚀 Federated Object Detection with YOLOv8 and Flower

Collaboratively train YOLOv8 models on distributed datasets while preserving data privacy.

---

🌐 Table of Contents

• 🔒 Why Federated Learning?
• 🤖 Why YOLOv8?
• ⚙️ Tech Stack
• 📥 Installation
  • 1. Prerequisites
  • 2. Set Up Conda Environment
  • 3. Install Dependencies
• 📂 Dataset Preparation
  • 1. Download Preprocessed Data
  • 2. Partition Data for Federated Clients
• 🚀 Quick Start
  • 1. Launch the Flower Server
  • 2. Start Federated Clients
  • 3. Monitor Training
• 🎛️ Simulation Setup
  • 1. Set Up a Flower Simulation Project
  • 4. Clean Up
• 🏗️ Project Architecture
• 🛠️ Troubleshooting
• 📚 References
• 🗺️ Future Roadmap

---

🔒 Why Federated Learning?

• Data Privacy: Sensitive data (e.g., surveillance footage, vehicle sensors) stays on-device.
• Bandwidth Efficiency: Only model gradients (not raw images) are transmitted.
• Regulatory Compliance: Ideal for GDPR, HIPAA, or industry-specific data policies.
• Edge Optimization: Train models directly on edge devices (cameras, drones, IoT sensors).

---

🤖 Why YOLOv8?

• State-of-the-Art Performance: Outperforms YOLOv5 in accuracy and speed.
• Multi-Task Support: Object detection, segmentation, and classification.
• Scalability: Pre-trained models (yolov8n, yolov8s, etc.) for diverse hardware.
• Ease of Use: Simplified training API and extensive documentation.

---

⚙️ Tech Stack

• Frameworks: Flower (FL), Ultralytics YOLOv8
• Dataset: BDD100K (preprocessed and hosted on Google Drive)
• GPU Support: CUDA 11.x, NVIDIA Drivers
• Tools: Conda, Git, WSL2 (optional)

---

📥 Installation

1. Prerequisites

• Python 3.10+
• NVIDIA GPU with CUDA 11.8+
• Git and Conda

2. Set Up Conda Environment

conda create -n fl_yolov8 python=3.10 -y  
conda activate fl_yolov8  

3. Install Dependencies

# PyTorch with CUDA  
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118  

# YOLOv8 and Flower  
pip install ultralytics flwr[simulation]  

# Additional utilities  
pip install opencv-python numpy tqdm pyyaml  

---

📂 Dataset Preparation

1. Download Preprocessed Data

The BDD100K dataset (already in YOLOv8 format) is hosted on Google Drive:
🔗 Download Dataset

mkdir -p federated_yolov8/data  
mv ~/Downloads/bdd100k_yolov8.zip federated_yolov8/data/  
cd federated_yolov8/data  
unzip bdd100k_yolov8.zip  

2. Partition Data for Federated Clients

Split the dataset into client-specific subsets using split_clients.py:

python split_clients.py \  
    --source="data" \  
    --output="data_clients" \  
    --num_clients=10  

Each client directory requires a data.yaml file. Example for client_0:

train: ../client_0/train/images  
val: ../client_0/val/images  
nc: 13  # Number of classes  
names: ["car", "person", "bus", "traffic light", ...]  

---

🚀 Quick Start

1. Launch the Flower Server

python server.py --num_rounds=10 --batch_size=32  

2. Start Federated Clients

Open separate terminals for each client:

# Client 0  
python client.py --id=0 --data_path="data_clients/client_0/data.yaml"  

# Client 1  
python client.py --id=1 --data_path="data_clients/client_1/data.yaml"  

3. Monitor Training

• Server Logs: Global model accuracy, round duration, client participation.
• Client Logs: Local training loss, validation mAP, GPU utilization.

---

🎛️ Simulation Setup

Flower provides a simulation engine to test federated learning on a single machine.

1. Set Up a Flower Simulation Project

flwr new my-project --framework PyTorch --username flower  
cd my-project  
pip install -e .  
flwr run .  

4. Clean Up

Use Ctrl+C in each terminal to stop the processes.

---

🏗️ Project Architecture

File	Purpose
server.py	Flower server for aggregating client updates.
client.py	Flower client to train YOLOv8 locally.
simulation.py	Runs multiple simulated FL clients on a single machine.
train_config.py	Configures training hyperparameters.
split_clients.py	Partitions dataset into client-specific subsets.
label_utils.py	Converts BDD100K annotations to YOLO format.

---

🛠️ Troubleshooting

Issue	Solution
CUDA Out of Memory	Reduce BATCH_SIZE or use yolov8n.
No GPU Detected	Verify torch.cuda.is_available() and reinstall PyTorch with CUDA.
Dataset Path Errors	Ensure data.yaml paths match the client directory structure.
Dependency Conflicts	Use a fresh Conda environment.

---

📚 References

• YOLOv8: Ultralytics Documentation
• Flower: Official Documentation
• BDD100K: Dataset Paper

---

🗺️ Future Roadmap

1. Advanced FL Strategies: Implement FedProx/FedNova for non-IID data.
2. Edge Deployment: Optimize for NVIDIA Jetson/Raspberry Pi.
3. Real-Time Inference: On-device inference with periodic FL updates.
4. Multi-Task Learning: Add segmentation support with YOLOv8.

---

Thanks! 😊