mlsanityData to 7 Oct 2026

Vertical federated learning based on DFP and BFGS

arXiv 2101.09428 ↗First posted 23 Jan 2021cs.LG cs.DCcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 14 references that are in the map. First posted on arXiv 23 Jan 2021.

Builds on

14 of 14 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2019Federated Machine Learning: Concept and Applicationscited by 409
  7. 2018Federated Learning via Over-the-Air Computationcited by 215
  8. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  9. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 174
  10. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  11. 2018Blockchained On-Device Federated Learningcited by 105
  12. 2019First Analysis of Local GD on Heterogeneous Datacited by 80
2 more
  1. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 37
  2. 2020Distributed Additive Encryption and Quantization for Privacy Preserving Federated Deep Learningcited by 12

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Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.

  1. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  2. 2021A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learningcited by 10