The Skellam Mechanism for Differentially Private Federated Learning

11 October 2021

Papers citing "The Skellam Mechanism for Differentially Private Federated Learning"

21 / 71 papers shown

Title
Differentially private partitioned variational inference Mikko A. Heikkilä Matthew Ashman S. Swaroop Richard Turner Antti Honkela FedML 30 2 0 23 Sep 2022
FedMR: Fedreated Learning via Model Recombination Ming Hu Zhihao Yue Zhiwei Ling Xian Wei Mingsong Chen FedML 16 0 0 16 Aug 2022
The Poisson binomial mechanism for secure and private federated learning Wei-Ning Chen Ayfer Özgür Peter Kairouz FedML 16 2 0 09 Jul 2022
Pisces: Efficient Federated Learning via Guided Asynchronous Training Zhifeng Jiang Wei Wang Baochun Li Bo-wen Li FedML 27 24 0 18 Jun 2022
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 22 52 0 16 Jun 2022
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints Justin Whitehouse Zhiwei Steven Wu Aaditya Ramdas Ryan M. Rogers 11 9 0 15 Jun 2022
Distributed Differential Privacy in Multi-Armed Bandits Sayak Ray Chowdhury Xingyu Zhou 27 12 0 12 Jun 2022
Privacy Amplification via Shuffled Check-Ins Seng Pei Liew Satoshi Hasegawa Tsubasa Takahashi FedML 29 0 0 07 Jun 2022
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment Zhiwei Ling Zhihao Yue Jun Xia Ming Hu Ting Wang Mingsong Chen FedML 26 8 0 24 May 2022
Protecting Data from all Parties: Combining FHE and DP in Federated Learning Arnaud Grivet Sébert Renaud Sirdey Oana Stan Cédric Gouy-Pailler FedML 18 0 0 09 May 2022
Privacy-Preserving Aggregation in Federated Learning: A Survey Ziyao Liu Jiale Guo Wenzhuo Yang Jiani Fan Kwok-Yan Lam Jun Zhao FedML 32 87 0 31 Mar 2022
Privacy-Aware Compression for Federated Data Analysis Kamalika Chaudhuri Chuan Guo Michael G. Rabbat FedML 27 27 0 15 Mar 2022
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Wei-Ning Chen Christopher A. Choquette-Choo Peter Kairouz A. Suresh FedML 39 63 0 07 Mar 2022
Pure Differential Privacy from Secure Intermediaries Albert Cheu Chao Yan FedML 25 9 0 19 Dec 2021
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 69 181 0 06 Dec 2021
Eluding Secure Aggregation in Federated Learning via Model Inconsistency Dario Pasquini Danilo Francati G. Ateniese FedML 28 101 0 14 Nov 2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation Eugene Bagdasaryan Peter Kairouz S. Mellem Adria Gascon Kallista A. Bonawitz D. Estrin Marco Gruteser 21 28 0 03 Nov 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 154 0 26 Feb 2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 37 76 0 25 Feb 2021
An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging C. Sabater A. Bellet J. Ramon FedML 21 18 0 12 Jun 2020
Device Heterogeneity in Federated Learning: A Superquantile Approach Yassine Laguel Krishna Pillutla J. Malick Zaïd Harchaoui FedML 40 22 0 25 Feb 2020