Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies

4 August 2023

Papers citing "Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies"

8 / 8 papers shown

Title
Federated Learning Enables Big Data for Rare Cancer Boundary Detection Sarthak Pati Ujjwal Baid Brandon Edwards Micah J. Sheller Shih-Han Wang ... Prashant Shah Bjoern Menze J. Barnholtz-Sloan Jason Martin Spyridon Bakas FedML AI4CE 101 214 0 22 Apr 2022
Decentralized Federated Learning through Proxy Model Sharing Shivam Kalra Junfeng Wen Jesse C. Cresswell M. Volkovs Hamid R. Tizhoosh FedML 51 96 0 22 Nov 2021
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 145 252 0 09 Sep 2021
Demystifying the Draft EU Artificial Intelligence Act Michael Veale Frederik J. Zuiderveen Borgesius 56 352 0 08 Jul 2021
Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models Lasse F. Wolff Anthony Benjamin Kanding Raghavendra Selvan HAI 54 312 0 06 Jul 2020
Federated Optimization: Distributed Machine Learning for On-Device Intelligence Jakub Konecný H. B. McMahan Daniel Ramage Peter Richtárik FedML 126 1,897 0 08 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 191 6,113 0 01 Jul 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 385 17,453 0 17 Feb 2016