SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning

“Towards making systems forget with machine unlearning,”

Yinzhi Cao and Junfeng Yang, · 2015

Earlier work this paper cites.

“Xgboost: A scalable tree boosting system,”

Tianqi Chen and Carlos Guestrin, · 2016

Earlier work this paper cites.

“On wasserstein two-sample testing and related families of nonparametric tests,”

Aaditya Ramdas, Nicolás García Trillos, and Marco Cuturi, · 2017

Earlier work this paper cites.

“General data protection regulation,”

Protection Regulation, · 2018

Earlier work this paper cites.

“The secret sharer: Evaluating and testing unintended memorization in neural networks,”

Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song, · 2019

Earlier work this paper cites.

“Certified data removal from machine learning models,”

Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten, · 2020

Earlier work this paper cites.

“Deltagrad: Rapid retraining of machine learning models,”

Yinjun Wu, Edgar Dobriban, and Susan Davidson, · 2020

Earlier work this paper cites.

“Extracting training data from large language models,”

Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al., · 2021

Earlier work this paper cites.

“Machine unlearning,”

Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot, · 2021

Cited alongside, same era.

“Adaptive machine unlearning,”

Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites, · 2021

Cited alongside, same era.

“Mixed-privacy forgetting in deep networks,”

Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto, · 2021

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“Descent-to-delete: Gradient-based methods for machine unlearning,”

Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi, · 2021

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“Federaser: Enabling efficient client-level data removal from federated learning models,”

Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu, · 2021

Cited alongside, same era.

“Secureboost: A lossless federated learning framework,”

Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Dimitrios Papadopoulos, and Qiang Yang, · 2021

“Machine unlearning for random forests,”

Jonathan Brophy and Daniel Lowd, · 2021

Later among the works it cites.

“Verifi: Towards verifiable federated unlearning,”

Xiangshan Gao, Xingjun Ma, Jingyi Wang, Youcheng Sun, Bo Li, Shouling Ji, Peng Cheng, and Jiming Chen, · 2022

Later among the works it cites.

“The right to be forgotten in federated learning: An efficient realization with rapid retraining,”

Yi Liu, Lei Xu, Xingliang Yuan, Cong Wang, and Bo Li, · 2022

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“A coupled design of exploiting record similarity for practical vertical federated learning,”

Zhaomin Wu, Qinbin Li, and Bingsheng He, · 2022

Later among the works it cites.

“Sgboost: An efficient and privacy-preserving vertical federated tree boosting framework,”

Jiaqi Zhao, Hui Zhu, Wei Xu, Fengwei Wang, Rongxing Lu, and Hui Li, · 2022

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“Deltaboost: Gradient boosting decision trees with efficient machine unlearning,”