Differential Privacy in Hierarchical Federated Learning: A Formal
  Analysis and Evaluation
Frank Po-Chen Lin Christopher Brinton
While federated learning (FL) eliminates the transmission of raw data over a
network, it is still vulnerable to privacy breaches from the communicated model
parameters. In this work, we formalize Differentially Private Hierarchical
Federated Learning (DP-HFL), a DP-enhanced FL methodology that seeks to improve
the privacy-utility tradeoff inherent in FL. Building upon recent proposals for
Hierarchical Differential Privacy (HDP), one of the key concepts of DP-HFL is
adapting DP noise injection at different layers of an established FL hierarchy
-- edge devices, edge servers, and cloud servers -- according to the trust
models within particular subnetworks. We conduct a comprehensive analysis of
the convergence behavior of DP-HFL, revealing conditions on parameter tuning
under which the model training process converges sublinearly to a stationarity
gap, with this gap depending on the network hierarchy, trust model, and target
privacy level. Subsequent numerical evaluations demonstrate that DP-HFL obtains
substantial improvements in convergence speed over baselines for different
privacy budgets, and validate the impact of network configuration on training.