no code implementations • 29 Feb 2024 • Ziqin Chen, Yongqiang Wang
We first discuss cryptography, differential privacy, and other techniques that can be used for privacy preservation and indicate their pros and cons for privacy protection in distributed optimization and learning.
no code implementations • 24 Oct 2023 • Ziqin Chen, Yongqiang Wang
To the best of our knowledge, this is the first result that simultaneously ensures learning accuracy and rigorous local differential privacy in distributed online learning over directed graphs.
no code implementations • 25 Jun 2023 • Ziqin Chen, Yongqiang Wang
Distributed online learning is gaining increased traction due to its unique ability to process large-scale datasets and streaming data.