On Differentially Private Federated Linear Contextual Bandits

27 February 2023

Papers citing "On Differentially Private Federated Linear Contextual Bandits"

8 / 8 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 48 0 0 21 Feb 2025
FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients Zhuohua Li Maoli Liu J. C. Lui FedML 29 5 0 05 May 2024
Harnessing the Power of Federated Learning in Federated Contextual Bandits Chengshuai Shi Ruida Zhou Kun Yang Cong Shen FedML 21 0 0 26 Dec 2023
Concurrent Shuffle Differential Privacy Under Continual Observation J. Tenenbaum Haim Kaplan Yishay Mansour Uri Stemmer FedML 28 2 0 29 Jan 2023
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 56 49 0 02 Oct 2022
When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits Achraf Azize D. Basu 23 21 0 06 Sep 2022
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
Mechanism Design in Large Games: Incentives and Privacy Michael Kearns Mallesh M. Pai Aaron Roth Jonathan R. Ullman 82 182 0 17 Jul 2012