Time-Distributed Backdoor Attacks on Federated Spiking Learning

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 …

advantages attacks backdoor cs.cr cs.cv cs.ne data devices distributed efficiency federated learning low networks neural networks neuromorphic paper privacy spiking neural networks systems vulnerability