A Robust Adversary Detection-Deactivation Method for Metaverse-oriented
  Collaborative Deep Learning
Pengfei Li Zhibo Zhang Ameena S. Al-Sumaiti Naoufel Werghi Chan Yeob Yeun
Metaverse is trending to create a digital circumstance that can transfer the
real world to an online platform supported by large quantities of real-time
interactions. Pre-trained Artificial Intelligence (AI) models are demonstrating
their increasing capability in aiding the metaverse to achieve an excellent
response with negligible delay, and nowadays, many large models are
collaboratively trained by various participants in a manner named collaborative
deep learning (CDL). However, several security weaknesses can threaten the
safety of the CDL training process, which might result in fatal attacks to
either the pre-trained large model or the local sensitive data sets possessed
by an individual entity. In CDL, malicious participants can hide within the
major innocent and silently uploads deceptive parameters to degenerate the
model performance, or they can abuse the downloaded parameters to construct a
Generative Adversarial Network (GAN) to acquire the private information of
others illegally. To compensate for these vulnerabilities, this paper proposes
an adversary detection-deactivation method, which can limit and isolate the
access of potential malicious participants, quarantine and disable the
GAN-attack or harmful backpropagation of received threatening gradients. A
detailed protection analysis has been conducted on a Multiview CDL case, and
results show that the protocol can effectively prevent harmful access by
heuristic manner analysis and can protect the existing model by swiftly
checking received gradients using only one low-cost branch with an embedded
firewall.