Recover User’s Private Training Image Data by Gradient in Federated Learning
Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that gradient inversion technique can reconstruct the in...
Main Authors: | Haimei Gong, Liangjun Jiang, Xiaoyang Liu, Yuanqi Wang, Lei Wang, Ke Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-09-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/22/19/7157 |
Similar Items
-
Evaluating the Impact of Mobility on Differentially Private Federated Learning
by: Eun-ji Kim, et al.
Published: (2024-06-01) -
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data
by: Joceline Ziegler, et al.
Published: (2022-07-01) -
MPHM: Model poisoning attacks on federal learning using historical information momentum
by: Shi Lei, et al.
Published: (2023-01-01) -
A Critical Evaluation of Privacy and Security Threats in Federated Learning
by: Muhammad Asad, et al.
Published: (2020-12-01) -
Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives
by: Pengrui Liu, et al.
Published: (2022-02-01)