Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift

23 November 2021

Papers citing "Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift"

3 / 3 papers shown

Title
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges Enrique Tomás Martínez Beltrán Mario Quiles Pérez Pedro Miguel Sánchez Sánchez Sergio López Bernal Gérome Bovet M. Pérez Gregorio Martínez Pérez Alberto Huertas Celdrán FedML 45 226 0 15 Nov 2022
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training Andrew Straiton Hard Kurt Partridge Neng Chen S. Augenstein Aishanee Shah ... Sara Ng Jessica Nguyen Ignacio López Moreno Rajiv Mathews F. Beaufays FedML 24 14 0 11 Apr 2022
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 187 412 0 14 Jul 2021