What is recorded here?
Applied tree aggregation to clipped cumulative gradients in FTRL, using correlated noise across updates to obtain private online/deep learning without requiring privacy amplification by sampling or shuffling.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits FTRL, continual-release trees and prior private online learning. Privacy does not require convex losses, but regret/risk utility theorems do; momentum variant has empirical benefits without a formal regret guarantee in this version. Repeated participation/multiple passes require additional accounting, not unrestricted data reuse. Pinned v1 displays a 2025 cover date despite a 2021 arXiv release stamp; date anomaly recorded, not treated as later invention.
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People and systems
Peter Kairouz; Brendan McMahan; Shuang Song; Om Thakkar; Abhradeep Thakurta; Zheng Xu
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
DP-FTRL: correlated-noise private training without sampling amplification
Applied tree aggregation to clipped cumulative gradients in FTRL, using correlated noise across updates to obtain private online/deep learning without requiring privacy amplification by sampling or shuffling.
Scientific scope
Inherits FTRL, continual-release trees and prior private online learning. Privacy does not require convex losses, but regret/risk utility theorems do; momentum variant has empirical benefits without a formal regret guarantee in this version. Repeated participation/multiple passes require additional accounting, not unrestricted data reuse. Pinned v1 displays a 2025 cover date despite a 2021 arXiv release stamp; date anomaly recorded, not treated as later invention.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Peter Kairouz; Brendan McMahan; Shuang Song; Om Thakkar; Abhradeep Thakurta; Zheng Xu
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.