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Differentially-Private Hierarchical Federated Learning
April 22, 2024, 4:43 a.m. | Frank Po-Chen Lin, Christopher Brinton
cs.LG updates on arXiv.org arxiv.org
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 \underline{H}ierarchical \underline{F}ederated Learning with \underline{H}ierarchical \underline{D}ifferential \underline{P}rivacy ({\tt H$^2$FDP}), a DP-enhanced FL methodology for jointly optimizing privacy and performance in hierarchical networks. Building upon recent proposals for Hierarchical Differential Privacy (HDP), one of the key concepts of {\tt H$^2$FDP} is adapting DP noise injection at …
abstract arxiv breaches cs.cr cs.dc cs.lg data federated learning hierarchical methodology network parameters performance privacy raw type vulnerable work
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