Private Non-Convex Federated Learning Without a Trusted Server

13 March 2022

Papers citing "Private Non-Convex Federated Learning Without a Trusted Server"

8 / 8 papers shown

Title
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds Zehan Zhu Yan Huang Xin Wang Jinming Xu 35 0 0 04 May 2024
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 23 5 0 29 Jan 2024
Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot Thomas Steinke 123 119 0 07 Oct 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 52 36 0 27 Sep 2021
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings Raef Bassily Cristóbal Guzmán Michael Menart 39 54 0 12 Jul 2021
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
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
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark W. Schmidt 119 1,194 0 16 Aug 2016