Project Florence Literature Review
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- Qiang Yang et al. Federated Machine Learning Concept and Applications
- Tian Li et al. Federated Learning Challenges, Methods, and Future Directions
- Daniele Romanini et al. PyVertical
- Record Linkage
- FederatedAI/FATE: An Industrial Grade Federated Learning Framework: Did they implement VFL. Yes. FATE/doc/2.0/fate/ml/hetero_secureboost_tutorial.ipynb at v2.0.0-beta · FederatedAI/FATE
- Nicola Rieke et al. The future of digital health with federated learning
- Qinbin Li et al. Federated Learning Systems. Vision, Hype, and Reality for Data Privacy and Protection
- Peter Kairouz et al. Advances and Open Problems in Federated Learning
- Jose Luis Ambite et al. Secure & Private Federated Neuroimaging
- Yuncheng Wu et al. Privacy-Preserving Vertical Federated Learning for Tree-based Models
- Xinjian Luo et al. Feature Inference Attack on Model Predictions in Vertical Federated Learning
- Yang Liu et al. Asymmetrical Vertical Federated Learning
- Tianyi Chen et al. VAFL a Method of Vertical Asynchronous Federated Learning
- Siwei Feng et al. Multi-Participant Multi-Class Vertical Federated Learning
- Yang Liu et al. Vertical Federated Learning
- Kang Wei et al. Vertical Federated Learning, Challenges, Methodologies and Experiments
- Shengwen Yang et al. Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
- Qiang Yang et al. Chapter 5 Vertical Federated Learning
- PSI
- A federated learning algorithm using a parallel-ensemble method on non-IID datasets | Complex & Intelligent Systems
- A Survey on Vertical Federated Learning: From a Layered Perspective
- Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
- Multi-Participant Vertical Federated Learning Based Time Series Prediction
- Federated Auto-Meta-Ensemble Learning Framework for AI-Enabled Military Operations
- Online Bagging and Boosting Definition, Online Bagging and Boosting Use in ML
- A survey on federated learning: challenges and applications
- Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study
- Achieving Differential Privacy in Vertically Partitioned Multiparty Learning
- Federated Learning for NLP
- Split Training, Vertical Partitioning, SplitNN-driven Vertical Partitioning
- Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
- Split Learning Project: MIT Media Lab
- SplitNN-driven Vertical Partitioning
- Iker Ceballos
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