Defending Against Free-Riders Attacks in Distributed Generative Adversarial Networks

Zilong Zhao*, Jiyue Huang, Lydia Y. Chen, Stefanie Roos

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Generative Adversarial Networks (GANs) are increasingly adopted by the industry to synthesize realistic images using competing generator and discriminator neural networks. Due to data not being centrally available, Multi-Discriminator (MD)-GANs training frameworks employ multiple discriminators that have direct access to the real data. Distributedly training a joint GAN model entails the risk of free-riders, i.e., participants that aim to benefit from the common model while only pretending to participate in the training process. In this paper, we first define a free-rider as a participant without training data and then identify three possible actions: not training, training on synthetic data, or using pre-trained models for similar but not identical tasks that are publicly available. We conduct experiments to explore the impact of these three types of free-riders on the ability of MD-GANs to produce images that are indistinguishable from real data. We consequently design a defense against free-riders, termed DFG, which compares the performance of client discriminators to reference discriminators at the server. The defense allows the server to evict clients whose behavior does not match that of a benign client. The result shows that even when 67% of the clients are free-riders, the proposed DFG can improve synthetic image quality by up to 70.96%, compared to the case of no defense.

Original languageEnglish
Title of host publicationFinancial Cryptography and Data Security - 27th International Conference, FC 2023, Revised Selected Papers
EditorsFoteini Baldimtsi, Christian Cachin
PublisherSpringer
Pages200-217
Number of pages18
ISBN (Print)9783031477508
DOIs
Publication statusPublished - 2024
Event27th International Conference on Financial Cryptography and Data Security, FC 2023 - Bol, Croatia
Duration: 1 May 20235 May 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13951
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Financial Cryptography and Data Security, FC 2023
Country/TerritoryCroatia
CityBol
Period1/05/235/05/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Anomaly detection
  • Defense
  • Free-rider attack
  • Multi-Discriminator GANs

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