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Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator.
Space-efficient online computation of quantile summaries
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Xgboost: A scalable tree boosting system supplementary material
Tianqi Chen and Carlos Guestrin · 2016
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, and Robert Deng · 2019
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