Project Florence Literature Review

• 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