FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Privacy Preserving Vertical Federated Learning

A curated list of Federated Learning papers/articles and recent advancements.

Papers related to Federated Learning in all top venues

Blockchain-based federated learning modular framework.

A curated list of advancements in Vertical Federated Learning, frameworks and libraries.

Stalactite is the framework implementing the vertical federated learning paradigm.

A coupled vertical federated learning framework that boosts the model performance with record similarities (NeurIPS 2022)

A demo of vertical federated learning on simple datasets

A vertical federated learning algorithm for classfication problems with gradient-based optimization.

DUAL: A Federated Unsupervised Anomaly Detection Framework for Collaborative Business Processes

Defend against Label Inference Attacks in Vertical Federated Learning via Label Compression

Vertical Federated Learning Case Study using APPFL

This project implements Federated Learning (FL) models—both Horizontal and Vertical—for diabetes prediction while ensuring secure client-server communication using Solidity-based smart contracts

Stochastic Participant Selection on Dynamic Vertical FL System via Data Padding

COVERT: private, communication-efficient vertical federated block-term tensor regression on clinical multi-view data (imaging, ECG, labs), at parity with centralized while transmitting only a DP-noised coupling score.

General Test-Time Backdoor Detection in Split Neural Network-Based Vertical Federated Learning

Private Coupled Descent: dimension-free differential privacy for vertical federated learning by privatizing the small shared coupling variable, with convergence guarantees, matching bounds, and synthetic validation.

''Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning''

A benchmark suite for Vertical Federated Unlearning (VFU), offering standardized datasets, evaluation metrics (accuracy, MIA, runtime, backdoor success), and paper references to unify and compare unlearning methods across modalities.