On the Security & Privacy in Federated Learning

On the Security & Privacy in Federated Learning

10 December 2021

Víctor Julio Ramírez-Durán

Papers citing "On the Security & Privacy in Federated Learning"

10 / 10 papers shown

Title
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers A. Boutet Thomas LeBrun Jan Aalmoes Adrien Baud FedML 24 13 0 26 Sep 2021
DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning Md Tamjid Hossain Shafkat Islam S. Badsha Haoting Shen AAML 24 25 0 21 Sep 2021
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning Hanchi Ren Jingjing Deng Xianghua Xie SILM AAML FedML 29 74 0 02 May 2021
Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation Ranwa Al Mallah David López Godwin Badu-Marfo Bilal Farooq AAML 21 29 0 24 Jan 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 245 1,386 0 14 Dec 2020
Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things Yi Liu Ruihui Zhao Jiawen Kang A. Yassine Dusit Niyato Jia-Jie Peng FedML 34 30 0 08 Dec 2020
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 33 120 0 10 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 164 392 0 04 Mar 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 158 878 0 29 Nov 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 237 2,899 0 04 Nov 2016