Welcome to XFL’s documentation!

XFL is a high-performance, high-flexibility, high-applicability, lightweight, open and easy-to-use Federated Learning framework. It supports a variety of federation models in both horizontal and vertical federation scenarios. To enable users to jointly train model legally and compliantly to unearth the value of their data, XFL adopts homomorphic encryption, differential privacy, secure multi-party computation and other security technologies to protect users’ local data from leakage, and applies secure communication protocols to ensure communication security.

Highlights:

• High-performance algorithm library

  • Comprehensive algorithms: support a variety of mainstream horizontal/vertical federation algorithms.

  • Excellent performance: significantly exceeds the average performance of federated learning products.

  • Network optimization: adapt to high latency, frequent packet loss, and unstable network environments.

• Flexible deployment

  • parties: support two-party/multi-party federated learning.

  • schedulering: any participant can act as a task scheduler.

  • hardware: support CPU/GPU/hybrid deployment.

• Lightweight, open and easy to use:

  • Lightweight: low requirements on host performance.

  • Open: support mainstream machine learning frameworks such as Pytorch, Tensorflow, PaddlePaddle and Jax, and user can conveniently design their own horizontal federation models.

  • Easy to use: able to run in both docker environment and Conda environment.

Function support

Function	Implementation
Horizontal Federation	✅
Vertical Federation	✅
XGBoost	✅
Deep Learning Framework	Pytorch/Tensorflow/PaddlePaddle/Jax
Partial Homomorphic Encryption	✅
Fully Homomorphic Encryption	✅
One Time Pad	✅
Secure Multi-party Computation	✅
Differential Privacy	✅
PSI(Private Set Intersection)	✅
PIR(Private Information Retrieval)	✅
GPU Support	✅
Cluster Deployment	✅
Online Inference	✅
Federated Node Management	✅
Federated Data Management	✅

TUTORIAL

• Introduction
  • Federated Learning
  • Participant Role
• Quickstart Guide
  • Prerequisites
  • Clone the repository
  • Using docker
  • Using Conda
  • Demonstration tutorial

ALGORITHMS

• Algorithms List
  • Notation
  • List of Algorithms
• Cryptographic Algorithm
  • Homomorphic Encryption
  • Cryptographic algorithms in secure aggregation
• Aggregation Algorithms
  • Introduction
  • Aggregation Type
• Differential Privacy
  • Introduction
  • Mathematical Definition
  • Differential Privacy in Federated Learning
• Horizontal Linear Regression
  • Introduction
  • Parameter List
• Horizontal Logistic Regression
  • Introduction
  • Parameter List
• Horizontal Poisson Regression
  • Introduction
  • Parameter List
• Horizontal ResNet
  • Introduction
  • Parameter List
• Horizontal DenseNet
  • Introduction
  • Parameter List
• Horizontal VGG
  • Introduction
  • Parameter List
• Horizontal Bert
  • Introduction
  • Parameter List
• Vertical Logistic Regression
  • Introduction
  • Parameter List
• Vertical Linear Regression
  • Introduction
  • Parameter List
• Vertical Poisson Regression
  • Introduction
  • Parameter List
• Vertical XGBoost
  • Introduction
  • Parameters List
• Vertical XGBoost Distributed
  • Introduction
  • Parameters List
• Vertical Binning Woe Iv
  • Introduction
  • Parameters List
• Vertical Feature Selection
  • Introduction
  • Parameters List
• Vertical K-means
  • Introduction
  • Parameters List
• Vertical Pearson
  • Introduction
  • Parameter List
• Vertical Sampler
  • Introduction
  • Parameters List
• Local Normalization
  • Introduction
  • Parameter List
• Local Standard Scaler
  • Introduction
  • Parameter List
• Local Data Split
  • Introduction
  • Parameter List
• Local Feature Preprocess
  • Introduction
  • Parameter List
• Local Data Statistic
  • Introduction
  • Parameter List

DEVELOPMENT

• API
  • Communication Module
  • Aggregation Module
• Development Guide
  • Create Operator File Structure
  • Create Operator Class
  • Develop of Operator
  • Tools for Development
  • Develop Horizontal Operator