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Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

29 February 2024
Tianyi Zhang
Yu Cao
Dianbo Liu
    FedML
ArXivPDFHTML

Papers citing "Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos"

33 / 33 papers shown
Title
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs
Huaiying Luo
Cheng Ji
FedML
32
3
0
19 May 2025
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing
Cheng Ji
Huaiying Luo
38
4
0
16 May 2025
FedFA: Federated Learning with Feature Anchors to Align Features and
  Classifiers for Heterogeneous Data
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
Tailin Zhou
Jun Zhang
Danny H. K. Tsang
FedML
55
60
0
17 Nov 2022
Addressing Heterogeneity in Federated Learning via Distributional
  Transformation
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Haolin Yuan
Bo Hui
Yuchen Yang
Philippe Burlina
Neil Zhenqiang Gong
Yinzhi Cao
FedML
OOD
52
13
0
26 Oct 2022
Virtual Homogeneity Learning: Defending against Data Heterogeneity in
  Federated Learning
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang
Yonggang Zhang
Shaoshuai Shi
Xinfu He
Bo Han
Xiaowen Chu
FedML
77
76
0
06 Jun 2022
FedBR: Improving Federated Learning on Heterogeneous Data via Local
  Learning Bias Reduction
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction
Yongxin Guo
Xiaoying Tang
Tao R. Lin
FedML
77
27
0
26 May 2022
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its
  applications on real-world medical records
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
Tianyi Zhang
Shirui Zhang
Ziwei Chen
Dianbo Liu
FedML
57
4
0
10 Dec 2021
Decentralized Federated Learning through Proxy Model Sharing
Decentralized Federated Learning through Proxy Model Sharing
Shivam Kalra
Junfeng Wen
Jesse C. Cresswell
M. Volkovs
Hamid R. Tizhoosh
FedML
59
96
0
22 Nov 2021
The Skellam Mechanism for Differentially Private Federated Learning
The Skellam Mechanism for Differentially Private Federated Learning
Naman Agarwal
Peter Kairouz
Ziyu Liu
FedML
68
124
0
11 Oct 2021
A Survey of Uncertainty in Deep Neural Networks
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDL
UQCV
OOD
229
1,149
0
07 Jul 2021
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep
  Learning
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado
Neil Band
Mark Collier
Josip Djolonga
Michael W. Dusenberry
...
D. Sculley
Balaji Lakshminarayanan
Jasper Snoek
Y. Gal
Dustin Tran
UQCV
ELM
71
96
0
07 Jun 2021
DEUP: Direct Epistemic Uncertainty Prediction
DEUP: Direct Epistemic Uncertainty Prediction
Salem Lahlou
Moksh Jain
Hadi Nekoei
V. Butoi
Paul Bertin
Jarrid Rector-Brooks
Maksym Korablyov
Yoshua Bengio
PER
UQLM
UQCV
UD
255
89
0
16 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with
  Secure Aggregation
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
Peter Kairouz
Ziyu Liu
Thomas Steinke
FedML
77
240
0
12 Feb 2021
Prevalence of Neural Collapse during the terminal phase of deep learning
  training
Prevalence of Neural Collapse during the terminal phase of deep learning training
Vardan Papyan
Xuemei Han
D. Donoho
194
574
0
18 Aug 2020
An Efficient Framework for Clustered Federated Learning
An Efficient Framework for Clustered Federated Learning
Avishek Ghosh
Jichan Chung
Dong Yin
Kannan Ramchandran
FedML
68
858
0
07 Jun 2020
Advances and Open Problems in Federated Learning
Advances and Open Problems in Federated Learning
Peter Kairouz
H. B. McMahan
Brendan Avent
A. Bellet
M. Bennis
...
Zheng Xu
Qiang Yang
Felix X. Yu
Han Yu
Sen Zhao
FedML
AI4CE
240
6,252
0
10 Dec 2019
Analyzing the Role of Model Uncertainty for Electronic Health Records
Analyzing the Role of Model Uncertainty for Electronic Health Records
Michael W. Dusenberry
Dustin Tran
Edward Choi
Jonas Kemp
Jeremy Nixon
Ghassen Jerfel
Katherine A. Heller
Andrew M. Dai
51
118
0
10 Jun 2019
Uncertainty-based Continual Learning with Adaptive Regularization
Uncertainty-based Continual Learning with Adaptive Regularization
Hongjoon Ahn
Sungmin Cha
Donggyu Lee
Taesup Moon
BDL
68
215
0
28 May 2019
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed
  Learning from Heterogeneous Datasets
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
Liping Li
Canran Xu
Xiangnan He
Yixin Cao
Tat-Seng Chua
FedML
109
595
0
09 Nov 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
131
1,536
0
08 Nov 2018
Federated Learning with Non-IID Data
Federated Learning with Non-IID Data
Yue Zhao
Meng Li
Liangzhen Lai
Naveen Suda
Damon Civin
Vikas Chandra
FedML
152
2,568
0
02 Jun 2018
Federated Meta-Learning with Fast Convergence and Efficient
  Communication
Federated Meta-Learning with Fast Convergence and Efficient Communication
Fei Chen
Mi Luo
Zhenhua Dong
Zhenguo Li
Xiuqiang He
FedML
81
395
0
22 Feb 2018
Differentially Private Federated Learning: A Client Level Perspective
Differentially Private Federated Learning: A Client Level Perspective
Robin C. Geyer
T. Klein
Moin Nabi
FedML
118
1,294
0
20 Dec 2017
Neural Discrete Representation Learning
Neural Discrete Representation Learning
Aaron van den Oord
Oriol Vinyals
Koray Kavukcuoglu
BDL
SSL
OCL
226
5,008
0
02 Nov 2017
Deeper, Broader and Artier Domain Generalization
Deeper, Broader and Artier Domain Generalization
Da Li
Yongxin Yang
Yi-Zhe Song
Timothy M. Hospedales
OOD
124
1,445
0
09 Oct 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer
  Vision?
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall
Y. Gal
BDL
OOD
UD
UQCV
PER
352
4,705
0
15 Mar 2017
Multiplicative Normalizing Flows for Variational Bayesian Neural
  Networks
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Christos Louizos
Max Welling
BDL
144
460
0
06 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
831
5,811
0
05 Dec 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
133
1,897
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
397
17,468
0
17 Feb 2016
Domain Generalization for Object Recognition with Multi-task
  Autoencoders
Domain Generalization for Object Recognition with Multi-task Autoencoders
Muhammad Ghifary
W. Kleijn
Mengjie Zhang
David Balduzzi
ViT
OOD
67
660
0
31 Aug 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
818
9,306
0
06 Jun 2015
Data classification using the Dempster-Shafer method
Data classification using the Dempster-Shafer method
Qi Chen
Amanda M. Whitbrook
U. Aickelin
C. Roadknight
49
52
0
02 Sep 2014
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