Privacy amplification for wireless federated learning with Rényi differential privacy and subsampling

DOI10.3934/ERA.2023356OpenAlexW4388130980MaRDI QIDQ6153177

Yaguan Qian, Xujun Che, Shu-Hui Wu, Qingjie Tan, Yuan-Hong Tao

Publication date: 13 February 2024

Published in: Electronic Research Archive (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/era.2023356

zbMATH Keywords

subsamplingfederated learningRényi differential privacywireless transmission scheme

Mathematics Subject Classification ID

Theory of data (68Pxx) Theory of computing (68Qxx)

Cites Work

• What Can We Learn Privately?
• The Algorithmic Foundations of Differential Privacy
• Our Data, Ourselves: Privacy Via Distributed Noise Generation
• Theory of Cryptography

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