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


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
Number of pages18
ISBN (Print)9783031477508
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)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Financial Cryptography and Data Security, FC 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
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.


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


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