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Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

Jan 27, 2021
Yige Li, Xixiang Lyu, Nodens Koren, Lingjuan Lyu, Bo Li, Xingjun Ma

* 19 pages, 14 figures, ICLR 2021 

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Privacy and Robustness in Federated Learning: Attacks and Defenses

Dec 07, 2020
Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu

* arXiv admin note: text overlap with arXiv:2003.02133; text overlap with arXiv:1911.11815 by other authors 

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Towards Building a Robust and Fair Federated Learning System

Nov 20, 2020
Xinyi Xu, Lingjuan Lyu

* In submission, under review 

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Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Oct 03, 2020
Lingjuan Lyu, Xuanli He, Yitong Li

* accepted to Findings of EMNLP 2020 

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Federated Model Distillation with Noise-Free Differential Privacy

Sep 11, 2020
Lichao Sun, Lingjuan Lyu

* under submission 

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Collaborative Fairness in Federated Learning

Aug 28, 2020
Lingjuan Lyu, Xinyi Xu, Qian Wang

* accepted to FL-IJCAI'20 workshop 

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How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

Jul 18, 2020
Lingjuan Lyu, Yitong Li, Karthik Nandakumar, Jiangshan Yu, Xingjun Ma

* Accepted for publication in TDSC 

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Towards Differentially Private Text Representations

Jun 25, 2020
Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao

* Accepted to SIGIR'20 

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Local Differential Privacy based Federated Learning for Internet of Things

Apr 19, 2020
Yang Zhao, Jun Zhao, Mengmeng Yang, Teng Wang, Ning Wang, Lingjuan Lyu, Dusit Niyato, Kwok Yan Lam

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Threats to Federated Learning: A Survey

Mar 04, 2020
Lingjuan Lyu, Han Yu, Qiang Yang

* 7 pages, 4 figures, 2 tables 

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Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain

Jun 04, 2019
Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin

* 13 pages, 5 figures, 6 tables 

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