How To Backdoor Federated Learning

2 July 2018

Papers citing "How To Backdoor Federated Learning"

49 / 349 papers shown

Title
Subpopulation Data Poisoning Attacks Matthew Jagielski Giorgio Severi Niklas Pousette Harger Alina Oprea AAML SILM 24 114 0 24 Jun 2020
Federated Learning Meets Multi-objective Optimization Zeou Hu Kiarash Shaloudegi Guojun Zhang Yaoliang Yu FedML 23 89 0 20 Jun 2020
Robust Federated Recommendation System Chen Chen Jingfeng Zhang A. Tung Mohan Kankanhalli Gang Chen FedML 46 26 0 15 Jun 2020
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data Chengxu Yang Qipeng Wang Mengwei Xu Shangguang Wang Kaigui Bian Yunxin Liu Xuanzhe Liu 24 22 0 12 Jun 2020
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing T. Ryffel Pierre Tholoniat D. Pointcheval Francis R. Bach FedML 30 94 0 08 Jun 2020
A Distributed Trust Framework for Privacy-Preserving Machine Learning Will Abramson A. Hall Pavlos Papadopoulos Nikolaos Pitropakis William J. Buchanan 14 20 0 03 Jun 2020
Blind Backdoors in Deep Learning Models Eugene Bagdasaryan Vitaly Shmatikov AAML FedML SILM 46 298 0 08 May 2020
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning Xinjian Luo Xiangqi Zhu FedML 78 25 0 27 Apr 2020
A Review of Privacy-preserving Federated Learning for the Internet-of-Things Christopher Briggs Zhong Fan Péter András 31 15 0 24 Apr 2020
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning Wenqi Wei Ling Liu Margaret Loper Ka-Ho Chow Mehmet Emre Gursoy Stacey Truex Yanzhao Wu FedML 26 147 0 22 Apr 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 60 50 0 01 Apr 2020
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training? Sharif Abuadbba Kyuyeon Kim Minki Kim Chandra Thapa S. Çamtepe Yansong Gao Hyoungshick Kim Surya Nepal FedML 8 123 0 16 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 204 436 0 04 Mar 2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping Sanghyun Hong Varun Chandrasekaran Yigitcan Kaya Tudor Dumitras Nicolas Papernot AAML 28 136 0 26 Feb 2020
FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC Rongfei Zeng Shixun Zhang Jiaqi Wang Xiaowen Chu FedML 32 180 0 22 Feb 2020
Anonymizing Data for Privacy-Preserving Federated Learning Olivia Choudhury A. Gkoulalas-Divanis Theodoros Salonidis I. Sylla Yoonyoung Park Grace Hsu Amar K. Das FedML 30 42 0 21 Feb 2020
Salvaging Federated Learning by Local Adaptation Tao Yu Eugene Bagdasaryan Vitaly Shmatikov FedML 25 261 0 12 Feb 2020
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts Max Ryabinin Anton I. Gusev FedML 27 48 0 10 Feb 2020
Learning to Detect Malicious Clients for Robust Federated Learning Suyi Li Yong Cheng Wei Wang Yang Liu Tianjian Chen AAML FedML 21 224 0 01 Feb 2020
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer Hong Chang Virat Shejwalkar Reza Shokri Amir Houmansadr FedML 26 167 0 24 Dec 2019
A Survey on Distributed Machine Learning Joost Verbraeken Matthijs Wolting Jonathan Katzy Jeroen Kloppenburg Tim Verbelen Jan S. Rellermeyer OOD 42 692 0 20 Dec 2019
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic Zhen Xiang David J. Miller Hang Wang G. Kesidis AAML 34 9 0 18 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 39 68 0 06 Nov 2019
Secure Federated Submodel Learning Chaoyue Niu Fan Wu Shaojie Tang Lifeng Hua Rongfei Jia Chengfei Lv Zhihua Wu Guihai Chen FedML 14 30 0 06 Nov 2019
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving Rulin Shao Hongyu Hè Hui Liu Dianbo Liu FedML OOD 25 13 0 23 Oct 2019
Abnormal Client Behavior Detection in Federated Learning Suyi Li Yong Cheng Yang Liu Wei Wang Tianjian Chen AAML 8 134 0 22 Oct 2019
Eavesdrop the Composition Proportion of Training Labels in Federated Learning Lixu Wang Shichao Xu Tianlin Li Qi Zhu FedML 25 63 0 14 Oct 2019
Detecting AI Trojans Using Meta Neural Analysis Xiaojun Xu Qi Wang Huichen Li Nikita Borisov Carl A. Gunter Bo Li 43 321 0 08 Oct 2019
Differential Privacy-enabled Federated Learning for Sensitive Health Data Olivia Choudhury A. Gkoulalas-Divanis Theodoros Salonidis I. Sylla Yoonyoung Park Grace Hsu Amar K. Das FedML OOD 28 175 0 07 Oct 2019
Federated User Representation Learning D. Bui Kshitiz Malik Jack Goetz Honglei Liu Seungwhan Moon Anuj Kumar Kang G. Shin FedML 30 63 0 27 Sep 2019
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging Luis Muñoz-González Kenneth T. Co Emil C. Lupu FedML 38 181 0 11 Sep 2019
Towards Effective Device-Aware Federated Learning Vito Walter Anelli Yashar Deldjoo Tommaso Di Noia Antonio Ferrara FedML 24 31 0 20 Aug 2019
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Bao Gia Doan Ehsan Abbasnejad Damith C. Ranasinghe AAML 27 66 0 09 Aug 2019
A Federated Learning Approach for Mobile Packet Classification Evita Bakopoulou Bálint Tillman A. Markopoulou 21 30 0 30 Jul 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 112 2,290 0 04 Jul 2019
Bypassing Backdoor Detection Algorithms in Deep Learning T. Tan Reza Shokri FedML AAML 39 149 0 31 May 2019
A backdoor attack against LSTM-based text classification systems Jiazhu Dai Chuanshuai Chen SILM 25 320 0 29 May 2019
Decentralized Learning of Generative Adversarial Networks from Non-iid Data Ryo Yonetani Tomohiro Takahashi Atsushi Hashimoto Yoshitaka Ushiku 45 24 0 23 May 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 24 1,337 0 07 Mar 2019
Evaluating Differentially Private Machine Learning in Practice Bargav Jayaraman David Evans 15 7 0 24 Feb 2019
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System Muhammad Ammad-ud-din E. Ivannikova Suleiman A. Khan Were Oyomno Qiang Fu K. E. Tan Adrian Flanagan FedML 37 270 0 29 Jan 2019
No Peek: A Survey of private distributed deep learning Praneeth Vepakomma Tristan Swedish Ramesh Raskar O. Gupta Abhimanyu Dubey SyDa FedML 32 100 0 08 Dec 2018
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 59 1,397 0 03 Dec 2018
Model-Reuse Attacks on Deep Learning Systems Yujie Ji Xinyang Zhang S. Ji Xiapu Luo Ting Wang SILM AAML 136 186 0 02 Dec 2018
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 191 1,034 0 29 Nov 2018
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation Micah J. Sheller G. A. Reina Brandon Edwards Jason Martin Spyridon Bakas FedML 29 457 0 10 Oct 2018
Mitigating Sybils in Federated Learning Poisoning Clement Fung Chris J. M. Yoon Ivan Beschastnikh AAML 15 497 0 14 Aug 2018
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning Tribhuvanesh Orekondy Seong Joon Oh Yang Zhang Bernt Schiele Mario Fritz PICV FedML 359 37 0 15 May 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 368 5,849 0 08 Jul 2016