Time-Distributed Backdoor Attacks on Federated Spiking Learning
Gorka Abad Stjepan Picek Aitor Urbieta
This paper investigates the vulnerability of spiking neural networks (SNNs)
and federated learning (FL) to backdoor attacks using neuromorphic data.
Despite the efficiency of SNNs and the privacy advantages of FL, particularly
in low-powered devices, we demonstrate that these systems are susceptible to
such attacks. We first assess the viability of using FL with SNNs using
neuromorphic data, showing its potential usage. Then, we evaluate the
transferability of known FL attack methods to SNNs, finding that these lead to
suboptimal attack performance. Therefore, we explore backdoor attacks involving
single and multiple attackers to improve the attack performance. Our primary
contribution is developing a novel attack strategy tailored to SNNs and FL,
which distributes the backdoor trigger temporally and across malicious devices,
enhancing the attack's effectiveness and stealthiness. In the best case, we
achieve a 100 attack success rate, 0.13 MSE, and 98.9 SSIM. Moreover, we adapt
and evaluate an existing defense against backdoor attacks, revealing its
inadequacy in protecting SNNs. This study underscores the need for robust
security measures in deploying SNNs and FL, particularly in the context of
backdoor attacks.