Protecting Privacy in Federated Time Series Analysis: A Pragmatic
Technology Review for Application Developers

Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers

28 August 2024

Daniel Bachlechner

Ruben Hetfleisch

Thomas Lorünser

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Papers citing "Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers"

7 / 7 papers shown

Title
Too Many Options: A Survey of ABE Libraries for Developers Aintzane Mosteiro-Sanchez Marc Barcelo Jasone Astorga A. Urbieta 51 4 0 26 Sep 2022
Faithful Edge Federated Learning: Scalability and Privacy Meng Zhang Ermin Wei R. Berry FedML 61 45 0 30 Jun 2021
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges Zhaohua Zheng Yize Zhou Yilong Sun Zhang Wang Boyi Liu Keqiu Li 82 109 0 02 Feb 2021
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 129 1,628 0 01 Nov 2019
Teechan: Payment Channels Using Trusted Execution Environments Joshua Lind Ittay Eyal Peter R. Pietzuch E. G. Sirer LRM 43 97 0 22 Dec 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 312 4,657 0 18 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 408 17,615 0 17 Feb 2016

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