Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offs

Federated machine learning enables model training across multiple participants while preserving data privacy. Vertical Federated Learning (VFL) handles scenarios in which participants have different feature sets for the same samples. Although Spiking Neural Networks (SNNs) offer efficiency advantages over Artificial Neural Networks (ANNs), their applicability in a VFL scenario remains unexplored. This paper examines SNNs in VFL, implementing and evaluating two architectures-with and without model splitting- using CIFAR-10 and CIFAR-100 datasets with VGG9 and ResNet models. The evaluation results show that SNNs achieve an accuracy comparable to that of ANNs in VFL while being significantly more energy efficient.