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Federated and Privacy-Preserving Learning of Accounting Data in
  Financial Statement Audits

Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits

26 August 2022
Marco Schreyer
Timur Sattarov
Damian Borth
    MLAU
ArXiv (abs)PDFHTML

Papers citing "Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits"

22 / 22 papers shown
Title
Federated Learning: Balancing the Thin Line Between Data Intelligence
  and Privacy
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy
Sherin M. Mathews
Samuel A. Assefa
FedML
31
5
0
22 Apr 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch
Opacus: User-Friendly Differential Privacy Library in PyTorch
Ashkan Yousefpour
I. Shilov
Alexandre Sablayrolles
Davide Testuggine
Karthik Prasad
...
Sayan Gosh
Akash Bharadwaj
Jessica Zhao
Graham Cormode
Ilya Mironov
VLM
286
368
0
25 Sep 2021
Multi-view Contrastive Self-Supervised Learning of Accounting Data
  Representations for Downstream Audit Tasks
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks
Marco Schreyer
Timur Sattarov
Damian Borth
MLAU
68
15
0
23 Sep 2021
See through Gradients: Image Batch Recovery via GradInversion
See through Gradients: Image Batch Recovery via GradInversion
Hongxu Yin
Arun Mallya
Arash Vahdat
J. Álvarez
Jan Kautz
Pavlo Molchanov
FedML
92
474
0
15 Apr 2021
Extracting Training Data from Large Language Models
Extracting Training Data from Large Language Models
Nicholas Carlini
Florian Tramèr
Eric Wallace
Matthew Jagielski
Ariel Herbert-Voss
...
Tom B. Brown
Basel Alomair
Ulfar Erlingsson
Alina Oprea
Colin Raffel
MLAUSILM
509
1,953
0
14 Dec 2020
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework
  Based on Adversarial Learning
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
Yu Zheng
Zhenyu Wu
Ye Yuan
Tianlong Chen
Zhangyang Wang
81
12
0
06 Oct 2020
Flower: A Friendly Federated Learning Research Framework
Flower: A Friendly Federated Learning Research Framework
Daniel J. Beutel
Taner Topal
Akhil Mathur
Xinchi Qiu
Javier Fernandez-Marques
...
Lorenzo Sani
Kwing Hei Li
Titouan Parcollet
Pedro Porto Buarque de Gusmão
Nicholas D. Lane
FedML
140
815
0
28 Jul 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
FedMLAI4CE
273
6,294
0
10 Dec 2019
Adversarial Learning of Deepfakes in Accounting
Adversarial Learning of Deepfakes in Accounting
Marco Schreyer
Timur Sattarov
Bernd Reimer
Damian Borth
AAML
54
26
0
09 Oct 2019
Detection of Accounting Anomalies in the Latent Space using Adversarial
  Autoencoder Neural Networks
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks
Marco Schreyer
Timur Sattarov
Christian Schulze
Bernd Reimer
Damian Borth
AAML
35
35
0
02 Aug 2019
Bayesian Nonparametric Federated Learning of Neural Networks
Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin
Mayank Agarwal
S. Ghosh
Kristjan Greenewald
T. Hoang
Y. Khazaeni
FedML
129
730
0
28 May 2019
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
1,546
0
08 Nov 2018
Distributed learning of deep neural network over multiple agents
Distributed learning of deep neural network over multiple agents
O. Gupta
Ramesh Raskar
FedMLOOD
74
606
0
14 Oct 2018
Differentially Private Empirical Risk Minimization with Input
  Perturbation
Differentially Private Empirical Risk Minimization with Input Perturbation
Kazuto Fukuchi
Quang Khai Tran
Jun Sakuma
59
36
0
20 Oct 2017
Detection of Anomalies in Large Scale Accounting Data using Deep
  Autoencoder Networks
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks
Marco Schreyer
Timur Sattarov
Damian Borth
Andreas Dengel
Bernd Reimer
61
106
0
15 Sep 2017
Federated Multi-Task Learning
Federated Multi-Task Learning
Virginia Smith
Chao-Kai Chiang
Maziar Sanjabi
Ameet Talwalkar
FedML
159
1,814
0
30 May 2017
Efficient Private ERM for Smooth Objectives
Efficient Private ERM for Smooth Objectives
Jiaqi Zhang
Kai Zheng
Wenlong Mou
Liwei Wang
47
146
0
29 Mar 2017
Semi-supervised Knowledge Transfer for Deep Learning from Private
  Training Data
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot
Martín Abadi
Ulfar Erlingsson
Ian Goodfellow
Kunal Talwar
94
1,021
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
145
1,909
0
08 Oct 2016
Deep Learning with Differential Privacy
Deep Learning with Differential Privacy
Martín Abadi
Andy Chu
Ian Goodfellow
H. B. McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
FedMLSyDa
216
2
0
01 Jul 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
408
17,615
0
17 Feb 2016
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OODSSL
278
12,460
0
24 Jun 2012
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