Skellam Mixture Mechanism: a Novel Approach to Federated Learning with
Differential Privacy

Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy

8 December 2022

Khin Mi Mi Aung

Papers citing "Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy"

10 / 10 papers shown

Title
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy Haotian Yang Z. Wang Benson Chou Sophie Xu Hao Wang Jingxian Wang Qizhen Zhang FedML 93 0 0 26 Mar 2025
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches Meifan Zhang Zhanhong Xie Lihua Yin FedML 24 1 0 08 Oct 2024
Calibrating Noise for Group Privacy in Subsampled Mechanisms Yangfan Jiang Xinjian Luo Yin Yang Xiaokui Xiao 31 2 0 19 Aug 2024
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs Yizheng Zhu Yuncheng Wu Zhaojing Luo Beng Chin Ooi Xiaokui Xiao 30 4 0 26 Nov 2023
A Survey for Federated Learning Evaluations: Goals and Measures Di Chai Leye Wang Liu Yang Junxue Zhang Kai Chen Qian Yang ELM FedML 17 21 0 23 Aug 2023
SparDL: Distributed Deep Learning Training with Efficient Sparse Communication Minjun Zhao Yichen Yin Yuren Mao Qing Liu Lu Chen Yunjun Gao 18 1 0 03 Apr 2023
Privacy Amplification via Shuffled Check-Ins Seng Pei Liew Satoshi Hasegawa Tsubasa Takahashi FedML 19 0 0 07 Jun 2022
Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning Chuan Ma Jun Li Kang Wei Bo Liu Ming Ding Long Yuan Zhu Han H. Vincent Poor 49 42 0 18 Feb 2022
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 132 119 0 07 Oct 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018