FLaVor (FL and FV) Developers' Guidebook
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Taiwan AI Labs - Federated Learning Platform: Developers' Guidebook (FLaVor)
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1 - Overview
1.1 System specification
1.2 Preparation for on-premises machine installation
1.3 Maintenance for system
1.4 Roles and Terminology
1.5 Task Flows
1.6 Roles, Tasks and Operations
1.7 How to start?
2 - Preparations
2.1 Federated learning and terminology
2.2 Docker tutorial for beginners
2.3 The input and output interfaces
2.4 Library compatibility
3 - Prepare an FL image by the assistance of FLaVor
3.1 An overview of FLaVor FL
3.1.1 A quick review of FL input and output interface
3.2 A Pytorch example
3.3 A Tensorflow (Keras) example
3.3.1 Tensorflow for large dataset
3.4 A XGBoost example
4 - Prepare an FV image by the assistance of FLaVor
4.1 An overview of FLaVor FV
4.1.1 A quick review of FV input and output interface
4.1.1 A quick review of FV input and output interface
4.2 A Pytorch example
5 - Porting models to AILabs FL / FV platform
5.1 Getting Started
6 - More
6.1 What is Taiwan AI Labs Federated Learning Framework?
6.2 What is Taiwan AI Labs Federated Learning Platform?
6.3 Dataflow of Taiwan AI Labs FL Framework
6.4 Taiwan AI Labs Federated Learning Edge Machine Security Compliance
6.5 Data Security Compliance
6.6 Code of AI Ethics
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2.4 Library compatibility
Docker images
pytorch/pytorch: 1.12.0-cuda11.3-cudnn8-runtime
tensorflow/tensorflow: 2.11.0-gpu
python 3.7-3.9. e.g. python: 3.8.16-slim
Pip library
opencv-python-headless
pip >= 20.0.2
xgboost >= 1.6