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Papaya: Practical, Private, and Scalable Federated Learning
8 November 2021
Dzmitry Huba
John Nguyen
Kshitiz Malik
Ruiyu Zhu
Michael G. Rabbat
Ashkan Yousefpour
Carole-Jean Wu
Hongyuan Zhan
Pavel Ustinov
H. Srinivas
Kaikai Wang
Anthony Shoumikhin
Jesik Min
Mani Malek
FedML
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Papers citing
"Papaya: Practical, Private, and Scalable Federated Learning"
5 / 5 papers shown
Title
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
Weiming Zhuang
Chen Chen
Lingjuan Lyu
C. L. P. Chen
Yaochu Jin
Lingjuan Lyu
AIFin
AI4CE
44
83
0
27 Jun 2023
Asynchronous Federated Learning on Heterogeneous Devices: A Survey
Chenhao Xu
Youyang Qu
Yong Xiang
Longxiang Gao
FedML
61
162
0
09 Sep 2021
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik
M. Seigel
Henry Mason
Dominic Telaar
Joris Kluivers
...
Dominic Hughes
O. Javidbakht
Fei Dong
Rehan Rishi
Stanley Hung
FedML
138
111
0
16 Feb 2021
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig
Nathalie Baracaldo
Gegi Thomas
Yi Zhou
Ali Anwar
...
Sean Laguna
Mikhail Yurochkin
Mayank Agarwal
Ebube Chuba
Annie Abay
FedML
100
123
0
22 Jul 2020
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
251
2,696
0
15 Sep 2016
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