Federated Forest

Abstract

Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the European Union's General Data Protection Regulation (GDPR) and China' Cyber Security Law. Such data islands situation and data privacy & security are two major challenges for applications of artificial intelligence. In this paper, we tackle these challenges and propose a privacy-preserving machine learning model, called Federated Forest, which is a lossless learning model of the traditional random forest method, i.e., achieving the same level of accuracy as the non-privacy-preserving approach. Based on it, we developed a secure cross-regional machine learning system that allows a learning process to be jointly trained over different regions' clients with the same user samples but different attribute sets, processing the data stored in each of them without exchanging their raw data. A novel prediction algorithm was also proposed which could largely reduce the communication overhead. Experiments on both real-world and UCI data sets demonstrate the performance of the Federated Forest is as accurate as the non-federated version. The efficiency and robustness of our proposed system had been verified. Overall, our model is practical, scalable and extensible for real-life tasks.

References in corpus (13)

• Federated Learning: Strategies for Improving Communication Efficiency
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Learning Differentially Private Recurrent Language Models
• Secure Federated Transfer Learning
• Federated Machine Learning: Concept and Applications
• Differentially Private Federated Learning: A Client Level Perspective
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• LEAF: A Benchmark for Federated Settings
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• SecureBoost: A Lossless Federated Learning Framework
• Federated Multi-Task Learning
• Federated Deep Reinforcement Learning
• Entity Resolution and Federated Learning get a Federated Resolution

Cited by in corpus (7)

• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
• Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges
• An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization
• The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
• Privacy preserving n-party scalar product protocol
• Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case
• Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data