This repo summarizes the private machine learning work of Xtra group. Currently we work mainly on two areas: federated learning and differential privacy. Federated learning enables the collaborative learning of multiple parties without exchanging the local data.
We have worked/are working on the following projects.
(1) Federated Learning Survey: We conducted a survey on federated learning systems.
(2) Federated Gradient Boosting Decision Trees: We designed a novel federated learning framework for gradient boosting decision trees.
(3) Differentially Private Gradient Boosting Decision Trees: We designed a differentially private gradient boosting decision tree training algorithm.
(4) Federated Learning Benchmarks: We designed a benchmark for evaluating the components in different FL systems.
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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Bingsheng He
arXiv preprint- We conducted a comprehensive analysis against existing federated learning systems from different aspects (see details).
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Practical Federated Gradient Boosting Decision Trees
Qinbin Li, Zeyi Wen, Bingsheng He
Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI 2020.- We proposed a novel federated learning framework for gradient boosting decision trees by exploiting similarity (see details).
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Privacy-Preserving Gradient Boosting Decision Trees
Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He
Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI 2020.- We designed a new differentially private gradient boosting decision trees training algorithm (see details).
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The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He
arXiv preprint.