Privacy-preserving federated learning on lattice quantization

DOI10.1142/s0219691323500200zbMath1520.62099MaRDI QIDQ6052296

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Publication date: 21 September 2023

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

zbMATH Keywords

differential privacylattice quantizationprivacy amplificationfederated learning

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Convex programming (90C25) Applications of mathematical programming (90C90) Privacy of data (68P27)

Cites Work

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• Convergence of stochastic proximal gradient algorithm
• The Algorithmic Foundations of Differential Privacy
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Robust Stochastic Approximation Approach to Stochastic Programming
• On universal quantization by randomized uniform/lattice quantizers
• On lattice quantization noise
• UVeQFed: Universal Vector Quantization for Federated Learning
• Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
• A Statistical Framework for Differential Privacy
• Stochastic distributed learning with gradient quantization and double-variance reduction
• Theory of Cryptography

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