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arXiv · 2401.11592

Differentially-Private Multi-Tier Federated Learning

Evan Chen·Frank Po-Chen Lin·Dong-Jun Han·Christopher G. Brinton
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Abstract

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 propose Multi-Tier Federated Learning with Multi-Tier Differential Privacy (M^2FDP), a DP-enhanced FL methodology for jointly optimizing privacy and performance in hierarchical networks. One of the key concepts of M^2FDP is to extend the concept of HDP towards Multi-Tier Differential Privacy (MDP), while also 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 M^2FDP, revealing conditions on parameter tuning under which the training process converges sublinearly to a finite stationarity gap that depends on the network hierarchy, trust model, and target privacy level. Subsequent numerical evaluations demonstrate that M^2FDP obtains substantial improvements in these metrics over baselines for different privacy budgets, and validate the impact of different system configurations.

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Evan Chen, Frank Po-Chen Lin, Dong-Jun Han, Christopher G. Brinton. 2024-01-21. Differentially-Private Multi-Tier Federated Learning. https://arxiv.org/abs/2401.11592

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