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2302.03098
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One-shot Empirical Privacy Estimation for Federated Learning
6 February 2023
Galen Andrew
Peter Kairouz
Sewoong Oh
Alina Oprea
H. B. McMahan
Vinith M. Suriyakumar
FedML
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Papers citing
"One-shot Empirical Privacy Estimation for Federated Learning"
8 / 8 papers shown
Title
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions
Flora Amato
Lingyu Qiu
Mohammad Tanveer
S. Cuomo
F. Giampaolo
F. Piccialli
FedML
55
0
0
05 May 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios
Sangyeon Yoon
Wonje Jeung
Albert No
85
0
0
02 Dec 2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
Thomas Steinke
Milad Nasr
Arun Ganesh
Borja Balle
Christopher A. Choquette-Choo
Matthew Jagielski
Jamie Hayes
Abhradeep Thakurta
Adam Smith
Andreas Terzis
23
7
0
08 Oct 2024
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective
Héber H. Arcolezi
Sébastien Gambs
25
1
0
04 Sep 2023
Composition of Differential Privacy & Privacy Amplification by Subsampling
Thomas Steinke
54
50
0
02 Oct 2022
Bounding Training Data Reconstruction in Private (Deep) Learning
Chuan Guo
Brian Karrer
Kamalika Chaudhuri
L. V. D. van der Maaten
103
53
0
28 Jan 2022
Practical and Private (Deep) Learning without Sampling or Shuffling
Peter Kairouz
Brendan McMahan
Shuang Song
Om Thakkar
Abhradeep Thakurta
Zheng Xu
FedML
178
154
0
26 Feb 2021
Extracting Training Data from Large Language Models
Nicholas Carlini
Florian Tramèr
Eric Wallace
Matthew Jagielski
Ariel Herbert-Voss
...
Tom B. Brown
D. Song
Ulfar Erlingsson
Alina Oprea
Colin Raffel
MLAU
SILM
267
1,808
0
14 Dec 2020
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