Title
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 51 1 0 14 Mar 2025
Segmented Private Data Aggregation in the Multi-message Shuffle Model Shaowei Wang Hongqiao Chen Sufen Zeng Ruilin Yang Hui Jiang ... Kaiqi Yu Rundong Mei Shaozheng Huang Wei Yang Bangzhou Xin FedML 43 0 0 31 Dec 2024
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 80 0 0 30 Nov 2024
Confidential Federated Computations Hubert Eichner Daniel Ramage Kallista A. Bonawitz Dzmitry Huba Tiziano Santoro ... Albert Cheu Katharine Daly Adria Gascon Marco Gruteser Brendan McMahan 29 2 0 16 Apr 2024
pfl-research: simulation framework for accelerating research in Private Federated Learning Filip Granqvist Congzheng Song Áine Cahill Rogier van Dalen Martin Pelikan Yi Sheng Chan Xiaojun Feng Natarajan Krishnaswami Vojta Jina Mona Chitnis FedML 26 5 0 09 Apr 2024
Momentum Approximation in Asynchronous Private Federated Learning Tao Yu Congzheng Song Jianyu Wang Mona Chitnis FedML 30 1 0 14 Feb 2024
Federated Learning with Differential Privacy for End-to-End Speech Recognition Martin Pelikan Sheikh Shams Azam Vitaly Feldman Jan Honza Silovsky Kunal Talwar Tatiana Likhomanenko 25 7 0 29 Sep 2023
PA-iMFL: Communication-Efficient Privacy Amplification Method against Data Reconstruction Attack in Improved Multi-Layer Federated Learning Jianhua Wang Xiaolin Chang Jelena Mivsić Vojislav B. Mivsić Zhi Chen Junchao Fan 26 2 0 25 Sep 2023
Invariant Aggregator for Defending against Federated Backdoor Attacks Xiaoya Wang Dimitrios Dimitriadis Oluwasanmi Koyejo Shruti Tople FedML 29 1 0 04 Oct 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 74 91 0 01 Feb 2022
When the Curious Abandon Honesty: Federated Learning Is Not Private Franziska Boenisch Adam Dziedzic R. Schuster Ali Shahin Shamsabadi Ilia Shumailov Nicolas Papernot FedML AAML 64 180 0 06 Dec 2021
Papaya: Practical, Private, and Scalable Federated Learning Dzmitry Huba John Nguyen Kshitiz Malik Ruiyu Zhu Michael G. Rabbat ... H. Srinivas Kaikai Wang Anthony Shoumikhin Jesik Min Mani Malek FedML 99 135 0 08 Nov 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 44 36 0 27 Sep 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 347 0 25 Sep 2021
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
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications Matthias Paulik M. Seigel Henry Mason Dominic Telaar Joris Kluivers ... Dominic Hughes O. Javidbakht Fei Dong Rehan Rishi Stanley Hung FedML 175 126 0 16 Feb 2021
Lightweight Techniques for Private Heavy Hitters Dan Boneh Elette Boyle Henry Corrigan-Gibbs N. Gilboa Yuval Ishai 24 105 0 29 Dec 2020
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
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 37 55 0 24 Sep 2019
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,031 0 29 Nov 2018
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 134 420 0 29 Nov 2018
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 112 395 0 08 Jun 2018
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 79 278 0 02 Oct 2017