# Vertical federated learning based on DFP and BFGS

arXiv: https://arxiv.org/abs/2101.09428 · HTML: https://mlsanity.com/paper/2101.09428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 14 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [First Analysis of Local GD on Heterogeneous Data](https://mlsanity.com/paper/1909.04715/) (2019)
- [A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression](https://mlsanity.com/paper/1912.00513/) (2019)
- [Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learning](https://mlsanity.com/paper/2011.12623/) (2020)

## Built on by (most cited first, 2 of 2)

- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning](https://mlsanity.com/paper/2106.06312/) (2021)

## Abstract (arXiv)

As data privacy is gradually valued by people, federated learning(FL) has emerged because of its potential to protect data. FL uses homomorphic encryption and differential privacy encryption on the promise of ensuring data security to realize distributed machine learning by exchanging encrypted information between different data providers. However, there are still many problems in FL, such as the communication efficiency between the client and the server and the data is non-iid. In order to solve the two problems mentioned above, we propose a novel vertical federated learning framework based on the DFP and the BFGS(denoted as BDFL), then apply it to logistic regression. Finally, we perform experiments using real datasets to test efficiency of BDFL framework.