all InfoSec news
Differentially-Private Hierarchical Federated Learning
April 22, 2024, 4:11 a.m. | Frank Po-Chen Lin, Christopher Brinton
cs.CR 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 …
a network arxiv breaches cs.cr cs.dc cs.lg data federated federated learning methodology network performance privacy private transmission vulnerable work
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Financial Crimes Compliance - Senior - Consulting - Location Open
@ EY | New York City, US, 10001-8604
Software Engineer - Cloud Security
@ Neo4j | Malmö
Security Consultant
@ LRQA | Singapore, Singapore, SG, 119963
Identity Governance Consultant
@ Allianz | Sydney, NSW, AU, 2000
Educator, Cybersecurity
@ Brain Station | Toronto
Principal Security Engineer
@ Hippocratic AI | Palo Alto