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Improving Federated Learning Personalization via Model Agnostic Meta
  Learning
v1v2 (latest)

Improving Federated Learning Personalization via Model Agnostic Meta Learning

27 September 2019
Yihan Jiang
Jakub Konecný
Keith Rush
Sreeram Kannan
    FedML
ArXiv (abs)PDFHTML

Papers citing "Improving Federated Learning Personalization via Model Agnostic Meta Learning"

20 / 20 papers shown
Title
Byzantine Resilient Federated Multi-Task Representation Learning
Byzantine Resilient Federated Multi-Task Representation Learning
Tuan Le
Shana Moothedath
102
0
0
24 Mar 2025
PeFLL: Personalized Federated Learning by Learning to Learn
PeFLL: Personalized Federated Learning by Learning to Learn
Jonathan Scott
Hossein Zakerinia
Christoph H. Lampert
FedML
219
12
0
17 Jan 2025
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models
Xiaochen Wang
Jiaqi Wang
Houping Xiao
Jianfei Chen
Fenglong Ma
MedIm
141
8
0
17 Aug 2024
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach
Mahrokh Ghoddousi Boroujeni
Andreas Krause
Giancarlo Ferrari-Trecate
FedML
105
3
0
16 Jan 2024
An Investigation Into On-device Personalization of End-to-end Automatic
  Speech Recognition Models
An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Models
K. Sim
P. Zadrazil
F. Beaufays
73
58
0
14 Sep 2019
Federated Learning: Challenges, Methods, and Future Directions
Federated Learning: Challenges, Methods, and Future Directions
Tian Li
Anit Kumar Sahu
Ameet Talwalkar
Virginia Smith
FedML
123
4,530
0
21 Aug 2019
Lookahead Optimizer: k steps forward, 1 step back
Lookahead Optimizer: k steps forward, 1 step back
Michael Ruogu Zhang
James Lucas
Geoffrey E. Hinton
Jimmy Ba
ODL
147
732
0
19 Jul 2019
On the Convergence of FedAvg on Non-IID Data
On the Convergence of FedAvg on Non-IID Data
Xiang Li
Kaixuan Huang
Wenhao Yang
Shusen Wang
Zhihua Zhang
FedML
147
2,343
0
04 Jul 2019
Adaptive Gradient-Based Meta-Learning Methods
Adaptive Gradient-Based Meta-Learning Methods
M. Khodak
Maria-Florina Balcan
Ameet Talwalkar
FedML
92
356
0
06 Jun 2019
Towards Federated Learning at Scale: System Design
Towards Federated Learning at Scale: System Design
Keith Bonawitz
Hubert Eichner
W. Grieskamp
Dzmitry Huba
A. Ingerman
...
H. B. McMahan
Timon Van Overveldt
David Petrou
Daniel Ramage
Jason Roselander
FedML
124
2,670
0
04 Feb 2019
Expanding the Reach of Federated Learning by Reducing Client Resource
  Requirements
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
S. Caldas
Jakub Konecný
H. B. McMahan
Ameet Talwalkar
71
450
0
18 Dec 2018
LEAF: A Benchmark for Federated Settings
LEAF: A Benchmark for Federated Settings
S. Caldas
Sai Meher Karthik Duddu
Peter Wu
Tian Li
Jakub Konecný
H. B. McMahan
Virginia Smith
Ameet Talwalkar
FedML
149
1,422
0
03 Dec 2018
Federated Learning for Mobile Keyboard Prediction
Federated Learning for Mobile Keyboard Prediction
Andrew Straiton Hard
Kanishka Rao
Zhifeng Lin
Swaroop Indra Ramaswamy
Youjie Li
S. Augenstein
Alex Schwing
M. Annavaram
A. Avestimehr
FedML
136
1,545
0
08 Nov 2018
How to train your MAML
How to train your MAML
Antreas Antoniou
Harrison Edwards
Amos Storkey
74
777
0
22 Oct 2018
Adaptive Communication Strategies to Achieve the Best Error-Runtime
  Trade-off in Local-Update SGD
Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGD
Jianyu Wang
Gauri Joshi
FedML
81
232
0
19 Oct 2018
On First-Order Meta-Learning Algorithms
On First-Order Meta-Learning Algorithms
Alex Nichol
Joshua Achiam
John Schulman
233
2,237
0
08 Mar 2018
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
825
11,937
0
09 Mar 2017
Federated Learning: Strategies for Improving Communication Efficiency
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konecný
H. B. McMahan
Felix X. Yu
Peter Richtárik
A. Suresh
Dave Bacon
FedML
306
4,649
0
18 Oct 2016
Federated Optimization: Distributed Machine Learning for On-Device
  Intelligence
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konecný
H. B. McMahan
Daniel Ramage
Peter Richtárik
FedML
143
1,902
0
08 Oct 2016
Communication-Efficient Learning of Deep Networks from Decentralized
  Data
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. B. McMahan
Eider Moore
Daniel Ramage
S. Hampson
Blaise Agüera y Arcas
FedML
406
17,559
0
17 Feb 2016
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