Defending Against Data Reconstruction Attacks in Federated Learning: An
Information Theory Approach

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach

2 March 2024

Qi Li

Papers citing "Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach"

5 / 5 papers shown

Title
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis Ruijun Deng Zhihui Lu Qiang Duan FedML 46 0 0 14 Apr 2025
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems Jiahui Chen Yi Zhao Qi Li Xuewei Feng Ke Xu AAML FedML 27 13 0 08 Oct 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 115 53 0 28 Jan 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 156 349 0 25 Sep 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 290 1,815 0 14 Dec 2020