A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
By Kairan Yang, Tao Fan, Tianjian Chen, Yuanming Shi, Qiang Yang. 2019
101 citations recorded by the source corpus.
Data privacy and security becomes a major concern in building machine learning models from different data providers. Federated learning shows promise by leaving data at providers locally and exchanging encrypted information. This paper studies the vertical federated learning structure for logistic regression where the data sets at two parties have the same sample IDs but own disjoint subsets of features. Existing frameworks adopt the first-order stochastic gradient descent algorithm, which requires large number of communication rounds. To address the communication challenge, we propose a quasi-Newton method based vertical federated learning framework for logistic regression under the additively homomorphic encryption scheme. Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round. Numerical results demonstrate the advantages of our approach over the first-order method.
Subfields: Artificial Intelligence
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Murali Annavaram
- Qiang Yang
- Chaoyang He
- Yang Liu
- S. Avestimehr
- Praneeth Vepakomma
- Jiankun Hu
- Yanming Zhu
Citations in the corpus
- SplitNN-driven Vertical Partitioning (2020, background): “The works in (Yang et al. , 2019a,b) provide schemes for secure vertically partitioned logistic regression based on homomorphic encryption.”
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “As demonstrated in Figure 3, FL has various topology definitions, such as vertical FL [34, 35, 36, 37, 38, 39, 40], split learning [41, 42], decentralized FL [43, 44, 45, 46], and Hierarchical FL [47, 48, 49, 50, 51, 52].”
- A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “[203] proposed a quasi-Newton method-based vertical FL framework for logistic regression, whose advantage is that it reduces the communication costs.”
- Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “At present, machine learning models are all built under the framework of a vertical Federated Learning system [135].”
- Communication-Efficient Edge AI: Algorithms and Systems (2020, background): “A number of federated learning algorithms [217], [218] were designed to break data isolation among institutions and to preserve the data privacy during edge training.”