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Enhancing Privacy of Spatiotemporal Federated Learning against Gradient
  Inversion Attacks
v1v2v3 (latest)

Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks

11 July 2024
Lele Zheng
Yang Cao
Renhe Jiang
Kenjiro Taura
Yulong Shen
Sheng Li
Masatoshi Yoshikawa
    AAML
ArXiv (abs)PDFHTML

Papers citing "Enhancing Privacy of Spatiotemporal Federated Learning against Gradient Inversion Attacks"

2 / 2 papers shown
Title
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning
Zhihao Zeng
Ziquan Fang
Wei Shao
Lu Chen
Yunjun Gao
FedML
106
0
0
08 May 2025
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
Zhihao Zeng
Ziquan Fang
Yuting Huang
Lu Chen
Yunjun Gao
249
0
0
15 Mar 2025
1