ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2109.08344
  4. Cited By
Achieving Model Fairness in Vertical Federated Learning
v1v2v3 (latest)

Achieving Model Fairness in Vertical Federated Learning

17 September 2021
Changxin Liu
Zhenan Fan
Zirui Zhou
Yang Shi
J. Pei
Lingyang Chu
Yong Zhang
    FedML
ArXiv (abs)PDFHTML

Papers citing "Achieving Model Fairness in Vertical Federated Learning"

19 / 19 papers shown
Title
Secure Bilevel Asynchronous Vertical Federated Learning with Backward
  Updating
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
Qingsong Zhang
Bin Gu
Cheng Deng
Heng-Chiao Huang
FedML
43
69
0
01 Mar 2021
Fairness-aware Agnostic Federated Learning
Fairness-aware Agnostic Federated Learning
Wei Du
Depeng Xu
Xintao Wu
Hanghang Tong
FedML
77
130
0
10 Oct 2020
VAFL: a Method of Vertical Asynchronous Federated Learning
VAFL: a Method of Vertical Asynchronous Federated Learning
Tianyi Chen
Xiao Jin
Yuejiao Sun
W. Yin
FedML
112
161
0
12 Jul 2020
A Unified Single-loop Alternating Gradient Projection Algorithm for
  Nonconvex-Concave and Convex-Nonconcave Minimax Problems
A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems
Zi Xu
Hui-Li Zhang
Yang Xu
Guanghui Lan
72
100
0
03 Jun 2020
Algorithmic Fairness
Algorithmic Fairness
Dana Pessach
E. Shmueli
FaML
63
390
0
21 Jan 2020
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated
  Learning
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Runhua Xu
Nathalie Baracaldo
Yi Zhou
Ali Anwar
Heiko Ludwig
FedML
59
288
0
12 Dec 2019
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
FedMLAI4CE
259
6,261
0
10 Dec 2019
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
Alekh Agarwal
Miroslav Dudík
Zhiwei Steven Wu
FaML
53
247
0
30 May 2019
Agnostic Federated Learning
Agnostic Federated Learning
M. Mohri
Gary Sivek
A. Suresh
FedML
136
935
0
01 Feb 2019
SecureBoost: A Lossless Federated Learning Framework
SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng
Tao Fan
Yilun Jin
Yang Liu
Tianjian Chen
Dimitrios Papadopoulos
Qiang Yang
FedML
103
582
0
25 Jan 2019
AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over
  Digraphs
AsySPA: An Exact Asynchronous Algorithm for Convex Optimization Over Digraphs
Jiaqi Zhang
Keyou You
44
74
0
13 Aug 2018
A Reductions Approach to Fair Classification
A Reductions Approach to Fair Classification
Alekh Agarwal
A. Beygelzimer
Miroslav Dudík
John Langford
Hanna M. Wallach
FaML
227
1,101
0
06 Mar 2018
Empirical Risk Minimization under Fairness Constraints
Empirical Risk Minimization under Fairness Constraints
Michele Donini
L. Oneto
Shai Ben-David
John Shawe-Taylor
Massimiliano Pontil
FaML
78
445
0
23 Feb 2018
Feature-Distributed SVRG for High-Dimensional Linear Classification
Feature-Distributed SVRG for High-Dimensional Linear Classification
Gong-Duo Zhang
Shen-Yi Zhao
Hao Gao
Wu-Jun Li
56
17
0
10 Feb 2018
Private federated learning on vertically partitioned data via entity
  resolution and additively homomorphic encryption
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Stephen Hardy
Wilko Henecka
Hamish Ivey-Law
Richard Nock
Giorgio Patrini
Guillaume Smith
Brian Thorne
FedML
68
536
0
29 Nov 2017
On Fairness and Calibration
On Fairness and Calibration
Geoff Pleiss
Manish Raghavan
Felix Wu
Jon M. Kleinberg
Kilian Q. Weinberger
FaML
200
880
0
06 Sep 2017
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
230
4,329
0
07 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
17,486
0
17 Feb 2016
Certifying and removing disparate impact
Certifying and removing disparate impact
Michael Feldman
Sorelle A. Friedler
John Moeller
C. Scheidegger
Suresh Venkatasubramanian
FaML
201
1,992
0
11 Dec 2014
1