Project Florence

Project Florence is a research project on VFL

• 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

2023-09-14

Can't we just ensemble them-?

1. Federated learning and split learning
   • Discuss split training versus federated learning
   • Federated learning converges to a better optimization point than ensembling independently trained models
   • Split learning involves training parts of the network at different sites
2. Vertical partitioning of data
   • Vertical partitioning of features across different sites can lead to poor individual predictors
   • Training a model that combines the data in a more sophisticated way may perform better
   • Focus on implementations that do not require training parts of the network at a central node
3. Next steps
   • Look into existing implementations of split learning and vertical partitioning
   • Focus on approaches using deep learning rather than classical models
   • Assume the record linkage problem is solved and focus on the training approach
4. Action items
   • Search for relevant papers that meet the criteria
   • Filter out papers using classical models instead of neural networks

2023-08-28

1. Vertical Federated Learning
   • The goal is to train a model using data from multiple sites without sharing the raw data.
   • Each site may have different features/columns in their data, but some overlap.
   • The challenge is training parts of the network using the data available at each site.
2. Record Linkage
   • Matching records across sites to identify which records represent the same entity.
   • Can be done using properties like name, address, phone number, and string similarity.
3. Inference
   • Once the model is trained, inference is done globally using all available data for an entity, not just at one site.
4. Potential Conferences
   • NeurIPS in May 2024 is a good target conference. Earlier deadlines may be too soon.
5. Meeting Plans
   • Thursdays at 2 p.m. at ISI or remotely if needed.