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 ยท 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
2023-09-14โ
Can't we just ensemble them-?
- 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
- 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
- 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
- Action items
- Search for relevant papers that meet the criteria
- Filter out papers using classical models instead of neural networks
2023-08-28โ
- 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.
- 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.
- Inference
- Once the model is trained, inference is done globally using all available data for an entity, not just at one site.
- Potential Conferences
- NeurIPS in May 2024 is a good target conference. Earlier deadlines may be too soon.
- Meeting Plans
- Thursdays at 2 p.m. at ISI or remotely if needed.