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Privacy-Preserving Distributed Optimization and Learning
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.
Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs
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.
Locally Differentially Private Distributed Online Learning with Guaranteed Optimality
Distributed online learning is gaining increased traction due to its unique ability to process large-scale datasets and streaming data.
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