Federated Learning Based on Kernel Local Differential Privacy and Low Gradient Sampling
Federated learning that is an approach to addressing the “data silo” problem in a collaborative fashion may face the risk of data leakage in real-world contexts. To solve this problem, we introduce the random Fourier feature mapping (RFFM) together with kernel local differentia...
Saved in:
Main Authors: | Yi Chen, Dan Chen, Niansheng Tang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10849528/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Privacy-enhanced federated learning scheme based on generative adversarial networks
by: Feng YU, et al.
Published: (2023-06-01) -
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy
by: Muhammad Adnan, et al.
Published: (2025-01-01) -
Stochastic gradient descent algorithm preserving differential privacy in MapReduce framework
by: Yihan YU, et al.
Published: (2018-01-01) -
Research on federated learning approach based on local differential privacy
by: Haiyan KANG, et al.
Published: (2022-10-01) -
Adaptive selection method of differential privacy GAN gradient clipping thresholds
by: Peng GUO, et al.
Published: (2018-05-01)