Optimal estimation in private distributed functional data analysis

Short summary

This paper introduces a novel distributed learning framework for functional data analysis that preserves differential privacy across multiple levels: user, server, and federated. It uses minimax theory to analyze privacy costs and achieve optimal estimation.

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