Differentially Private Federated Learning: A Systematic Review
Authors: Jie Fu, Yuan Hong, Xinpeng Ling, Leixia Wang, Xun Ran, Zhiyu Sun, Wendy Hui Wang, Zhili Chen, Yang Cao | Published: 2024-05-14 | Updated: 2024-05-20
Privacy Protection Method
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
Authors: Amin Aminifar, Matin Shokri, Amir Aminifar | Published: 2024-05-09 | Updated: 2024-09-13
Model Inversion Robustness: Can Transfer Learning Help?
Authors: Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran, Ngoc-Bao Nguyen, Ngai-Man Cheung | Published: 2024-05-09
Differentially Private Post-Processing for Fair Regression
Authors: Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao | Published: 2024-05-07
Locally Differentially Private In-Context Learning
Authors: Chunyan Zheng, Keke Sun, Wenhao Zhao, Haibo Zhou, Lixin Jiang, Shaoyang Song, Chunlai Zhou | Published: 2024-05-07 | Updated: 2024-05-08
IPFed: Identity protected federated learning for user authentication
Authors: Yosuke Kaga, Yusei Suzuki, Kenta Takahashi | Published: 2024-05-07
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
Authors: Joshua C. Zhao, Saurabh Bagchi, Salman Avestimehr, Kevin S. Chan, Somali Chaterji, Dimitris Dimitriadis, Jiacheng Li, Ninghui Li, Arash Nourian, Holger R. Roth | Published: 2024-05-06 | Updated: 2025-03-22
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Authors: Rachel Cummings, Shlomi Hod, Jayshree Sarathy, Marika Swanberg | Published: 2024-05-02
Evaluations of Machine Learning Privacy Defenses are Misleading
Authors: Michael Aerni, Jie Zhang, Florian Tramèr | Published: 2024-04-26 | Updated: 2024-09-05
OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processing
Authors: Songze Li, Mingzhe Liu, Mengqi Chen | Published: 2024-04-17