A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression

Abstract

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.

References in corpus (6)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Learning: Strategies for Improving Communication Efficiency
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• SecureBoost: A Lossless Federated Learning Framework
• A Linearly-Convergent Stochastic L-BFGS Algorithm
• Federated Learning via Over-the-Air Computation

Cited by in corpus (15)

• FedML: A Research Library and Benchmark for Federated Machine Learning
• Multi-Participant Multi-Class Vertical Federated Learning
• Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
• Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
• Communication-Efficient Edge AI: Algorithms and Systems
• A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
• FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
• A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring
• A Vertical Federated Learning Framework for Horizontally Partitioned Labels
• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
• Federated Learning on Non-IID Data: A Survey
• Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm
• Vertical federated learning based on DFP and BFGS
• Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
• Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning