
The authors propose novel algorithms for distributed estimation and learning that enable networked agents to collectively estimate unknown statistical properties of their privately observed samples while preserving the privacy of their signals and network neighborhoods.


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differentially-private-distributed-estimation-and-learning-balancing-privacy-and-accuracy-in-networked-environments


Differentially Private Distributed Estimation and Learning: Balancing Privacy and Accuracy in Networked Environments
