When the Curious Abandon Honesty: Federated Learning Is Not Private-Reference-Cited by-同舟云学术

When the Curious Abandon Honesty: Federated Learning Is Not Private

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)
language:
Short-container-title:

Author:

Boenisch Franziska¹,Dziedzic Adam¹,Schuster Roei¹,Shamsabadi Ali Shahin¹,Shumailov Ilia¹,Papernot Nicolas¹

Affiliation:

1. Vector Institute

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10190357/10190476/10190537.pdf?arnumber=10190537

Reference56 articles.

1. Perceptron-based learning algorithms

2. Deep Leakage from Gradients

3. Robbing the fed: Directly obtaining private data in federated learning with modified models;fowl;International Conference on Learning Representations,2021

4. Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Client-Side Gradient Inversion Attack in Federated Learning Using Secure Aggregation;IEEE Internet of Things Journal;2024-09-01

2. A Survey of Federated Learning for mmWave Massive MIMO;IEEE Internet of Things Journal;2024-08-15

3. Vertical Federated Learning Across Heterogeneous Regions for Industry 4.0;IEEE Transactions on Industrial Informatics;2024-08

4. OASIS: Offsetting Active Reconstruction Attacks in Federated Learning;2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS);2024-07-23

5. DPFLA: Defending Private Federated Learning Against Poisoning Attacks;IEEE Transactions on Services Computing;2024-07