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.

通过比较不同的数据聚合方法，本文对包含传统人工神经网络（ANNs）和脉冲神经网络（SNNs）的联邦学习系统进行了评估，实验结果表明 CNN-SNN 融合框架在 MNIST 数据集上表现最好，并发现了多模型联邦学习收敛过程中的竞争抑制现象。