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Related Experiment Videos

Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy

Arnab Auddy1, T Tony Cai2, Abhinav Chakraborty3

  • 1Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|July 16, 2026
PubMed
Summary

This study explores transfer learning for classification with differential privacy, finding optimal trade-offs between data privacy and accuracy. Adaptive methods achieve near-perfect classification rates under distributed, heterogeneous privacy constraints.

Keywords:
adaptive classifiercost of privacyminimax optimalityposterior driftrelative signal exponent

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Statistical Learning Theory
  • Data Privacy

Background:

  • Distributed learning systems face challenges with data heterogeneity and privacy.
  • Transfer learning aims to leverage knowledge from source tasks to improve performance on target tasks.
  • Differential privacy offers a rigorous framework for protecting individual data in statistical analysis.

Purpose of the Study:

  • To analyze minimax and adaptive transfer learning under distributed differential privacy.
  • To characterize the impact of heterogeneous data and privacy parameters on classification accuracy.
  • To develop an adaptive classifier that balances privacy and performance.

Main Methods:

  • Establishing minimax misclassification rates for nonparametric classification.
  • Analyzing the effects of sample sizes, privacy parameters, and data heterogeneity.
  • Developing and evaluating a data-driven adaptive classifier.
  • Utilizing simulation studies and real-world data applications.

Main Results:

  • The study precisely characterizes the minimax misclassification rate, revealing phase transitions.
  • Key trade-offs between privacy preservation and classification accuracy are identified.
  • The proposed adaptive classifier achieves near-optimal rates within a logarithmic factor.

Conclusions:

  • Distributed transfer learning with differential privacy is feasible and can be optimized.
  • Heterogeneity in data and privacy parameters significantly influences classification performance.
  • Adaptive strategies are effective in achieving robust and private classification in complex settings.