  Federated learning (FL) has emerged as a promising paradigm for training
models on decentralized data while safeguarding data privacy. Most existing FL
systems, however, assume that all machine learning models are of the same type,
although it becomes more likely that different edge devices adopt different
types of AI models, including both conventional analogue artificial neural
networks (ANNs) and biologically more plausible spiking neural networks (SNNs).
This diversity empowers the efficient handling of specific tasks and
requirements, showcasing the adaptability and versatility of edge computing
platforms. One main challenge of such heterogeneous FL system lies in
effectively aggregating models from the local devices in a privacy-preserving
manner. To address the above issue, this work benchmarks FL systems containing
both convoluntional neural networks (CNNs) and SNNs by comparing various
aggregation approaches, including federated CNNs, federated SNNs, federated
CNNs for SNNs, federated SNNs for CNNs, and federated CNNs with SNN fusion.
Experimental results demonstrate that the CNN-SNN fusion framework exhibits the
best performance among the above settings on the MNIST dataset. Additionally,
intriguing phenomena of competitive suppression are noted during the
convergence process of multi-model FL.
