  Federated machine learning enables model training across multiple clients
while maintaining data privacy. Vertical Federated Learning (VFL) specifically
deals with instances where the clients have different feature sets of the same
samples. As federated learning models aim to improve efficiency and
adaptability, innovative neural network architectures like Spiking Neural
Networks (SNNs) are being leveraged to enable fast and accurate processing at
the edge. SNNs, known for their efficiency over Artificial Neural Networks
(ANNs), have not been analyzed for their applicability in VFL, thus far. In
this paper, we investigate the benefits and trade-offs of using SNN models in a
vertical federated learning setting. We implement two different federated
learning architectures -- with model splitting and without model splitting --
that have different privacy and performance implications. We evaluate the setup
using CIFAR-10 and CIFAR-100 benchmark datasets along with SNN implementations
of VGG9 and ResNET classification models. Comparative evaluations demonstrate
that the accuracy of SNN models is comparable to that of traditional ANNs for
VFL applications, albeit significantly more energy efficient.
