Fusing deep learning models trained on separately located clients into a global model in a one-shot communication round is a straightforward implementation of Federated Learning. Although current model fusion methods are shown experimentally valid in fusing neural networks with almost identical architectures, they are rarely theoretically analyzed. In this paper, we reveal the phenomenon of neuron disturbing, where neurons from heterogeneous local models interfere with each other mutually. We give detailed explanations from a Bayesian viewpoint combining the data heterogeneity among clients and properties of neural networks. Furthermore, to validate our findings, we propose an experimental method that excludes neuron disturbing and fuses neural networks via adaptively selecting a local model, called AMS, to execute the prediction according to the input. The experiments demonstrate that AMS is more robust in data heterogeneity than general model fusion and ensemble methods. This implies the necessity of considering neural disturbing in model fusion. Besides, AMS is available for fusing models with varying architectures as an experimental algorithm, and we also list several possible extensions of AMS for future work.

翻译：将分别位于不同客户端上训练的深度学习模型通过单次通信回合融合为全局模型，是联邦学习的一种直接实现方式。尽管当前的模型融合方法在融合几乎相同架构的神经网络时已被实验验证有效，但很少有研究对其进行理论分析。本文揭示了神经元扰动现象——即异构局部模型中的神经元会相互干扰。我们从贝叶斯视角出发，结合客户端之间的数据异构性与神经网络特性给出了详细解释。此外，为了验证我们的发现，我们提出了一种实验方法，通过自适应选择局部模型（称为AMS）来排除神经元扰动并融合神经网络，根据输入执行预测。实验表明，AMS在数据异构性下比通用模型融合及集成方法具有更强鲁棒性，这证明了在模型融合中考虑神经元扰动的必要性。此外，AMS作为一种实验性算法可用于融合具有不同架构的模型，我们还列出了AMS在未来的若干可能扩展方向。