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Cited By

Randomized Quantization is All You Need for Differential Privacy in
Federated Learning

Randomized Quantization is All You Need for Differential Privacy in Federated Learning

20 June 2023

Jacob D. Abernethy

ArXiv (abs)PDF HTML Github

Papers citing "Randomized Quantization is All You Need for Differential Privacy in Federated Learning"

15 / 15 papers shown

FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity

FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity

Ferdinand Kahenga

116

0

0

23 Sep 2025

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling

Gennady Pekhimenko

Nandita Vijaykumar

194

0

0

03 Sep 2025

Federated learning over physical channels: adaptive algorithms with near-optimal guarantees

150

0

0

02 Sep 2025

Prompt Inversion Attack against Collaborative Inference of Large Language Models

Prompt Inversion Attack against Collaborative Inference of Large Language ModelsIEEE Symposium on Security and Privacy (S&P), 2025

553

11

0

12 Mar 2025

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

Philippe Formont

Pablo Piantanida

Elisabeth Gassiat

361

2

0

10 Feb 2025

PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy

PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy

Timothy Castiglia

Stacy Patterson

435

2

0

23 Jan 2025

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA

NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA

Mohamed Ali Souibgui

Rubèn Pérez Tito

Raouf Kerkouche

...

Dimosthenis Karatzas

417

3

0

06 Nov 2024

Scalable Differential Privacy Mechanisms for Real-Time Machine Learning
Applications

Scalable Differential Privacy Mechanisms for Real-Time Machine Learning Applications

268

1

0

16 Sep 2024

Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point

Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point

382

1

0

02 Jul 2024

A Quantization-based Technique for Privacy Preserving Distributed
Learning

A Quantization-based Technique for Privacy Preserving Distributed Learning

Maurizio Colombo

Ernesto Damiani

Lamees Mahmoud AlQassem

Al Anoud Almemari

Yousof Alhammadi

204

1

0

26 Jun 2024

Privacy-Aware Randomized Quantization via Linear Programming

Privacy-Aware Randomized Quantization via Linear Programming

Mohammad Mahdi Khalili

403

2

0

01 Jun 2024

Secure Aggregation is Not Private Against Membership Inference Attacks

Secure Aggregation is Not Private Against Membership Inference Attacks

Giuseppe Durisi

Alexandre Graell i Amat

515

11

0

26 Mar 2024

Layered Randomized Quantization for Communication-Efficient and
Privacy-Preserving Distributed Learning

Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning

Linqi Song

305

12

0

12 Dec 2023

FedECA: A Federated External Control Arm Method for Causal Inference
with Time-To-Event Data in Distributed Settings

FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed SettingsNature Communications (Nat. Commun.), 2023

Jean Ogier du Terrail

Quentin Klopfenstein

Nicolas Loiseau

Mohammad Hallal

Félix Balazard

406

2

0

28 Nov 2023

Communication-Efficient Laplace Mechanism for Differential Privacy via
Random Quantization

Communication-Efficient Laplace Mechanism for Differential Privacy via Random QuantizationIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023

Ali Moradi Shahmiri

266

19

0

13 Sep 2023

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