Practical Federated Gradient Boosting Decision Trees
By Qinbin Li, Zeyi Wen, Bingsheng He. 2019
220 citations recorded by the source corpus.
Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several recent studies on how to train GBDTs in the federated learning setting. In this paper, we focus on horizontal federated learning, where data samples with the same features are distributed among multiple parties. However, existing studies are not efficient or effective enough for practical use. They suffer either from the inefficiency due to the usage of costly data transformations such as secure sharing and homomorphic encryption, or from the low model accuracy due to differential privacy designs. In this paper, we study a practical federated environment with relaxed privacy constraints. In this environment, a dishonest party might obtain some information about the other parties' data, but it is still impossible for the dishonest party to derive the actual raw data of other parties. Specifically, each party boosts a number of trees by exploiting similarity information based on locality-sensitive hashing. We prove that our framework is secure without exposing the original record to other parties, while the computation overhead in the training process is kept low. Our experimental studies show that, compared with normal training with the local data of each party, our approach can significantly improve the predictive accuracy, and achieve comparable accuracy to the original GBDT with the data from all parties.
Subfields: Artificial Intelligence, Statistics and Probability
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Citations in the corpus
- SecureBoost: A Lossless Federated Learning Framework (2019, background): “Following the same idea, several attempts have been made to reinvent different machine learning models to the federated setting, including decision tree [20], [21], linear/logistic regression [22], [23], [24] and neural network [25], [26].”
- A Survey on Federated Learning for Resource-Constrained IoT Devices (2021, unknown): “In tree-based FL, a model is generated for training single or multiple DTs. Cheng et al. [25] and Li et al. [30] designed gradient boosting DTs (GBDTs) by considering both horizontal and vertical partitioned data schemes.”
- A review of applications in federated learning (2020, background): “In terms of optimization for grounding application, high communication cost, statistical and structural heterogeneity are major issues faced by researchers currently (Li et al., 2020).”
- Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, background): “Then, this subsection presents two different decentralized communications mechanisms: peer-to-peer (P2P) communications [67], [68], and gossip communications [70], [120].”
- Decentralized Federated Learning: A Survey and Perspective (2023, background): “DFL has received extensive attention as an emerging framework [55], [56], [57], [58], [59], [60].”
- A Comprehensive Survey of Privacy-preserving Federated Learning (2021, background): “[96] proposed a horizontal FL framework for gradient boosting decision trees to improve the model efficiency and accuracy.”
- A Communication Efficient Collaborative Learning Framework for Distributed Features (2019, background): “Such a security definition is inline with prior security definitions proposed in privacy-preserving machine learning and secure multiple computation (SMC), such as [27–29, 4, 30].”
- FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022, background): “With only one iteration ( T = 1 ), such a security definition is inline with prior security definitions proposed in privacy-preserving machine learning and secure multiple computation (SMC), such as [7], [31], [32], [33], [34].”
- Model-Contrastive Federated Learning (2021, background): “Federated learning has emerged as an important machine learning area and attracted many research interests [34, 28, 22, 25, 41, 5, 16, 2, 11].”
- Federated Learning on Non-IID Data Silos: An Experimental Study (2021, background): “It has attracted many research interests [7, 22, 31, 36, 39, 45, 64] and been widely used in practice [3, 20, 30].”
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “An example is SimFL [95], where each party trains a tree sequentially and sends the tree to all the other parties.”