Federated learning is a distributed machine learning approach in which clients train models locally with their own data and upload them to a server so that their trained results are shared between them without uploading raw data to the server. There are some challenges in federated learning, such as communication size reduction and client heterogeneity. The former can mitigate the communication overheads, and the latter can allow the clients to choose proper models depending on their available compute resources. To address these challenges, in this paper, we utilize Neural ODE based models for federated learning. The proposed flexible federated learning approach can reduce the communication size while aggregating models with different iteration counts or depths. Our contribution is that we experimentally demonstrate that the proposed federated learning can aggregate models with different iteration counts or depths. It is compared with a different federated learning approach in terms of the accuracy. Furthermore, we show that our approach can reduce communication size by up to 92.4% compared with a baseline ResNet model using CIFAR-10 dataset.

翻译：联邦学习是一种分布式机器学习方法，其中客户端使用自己的数据在本地训练模型，并将模型上传至服务器，从而实现训练结果的共享，而无需将原始数据上传至服务器。联邦学习面临一些挑战，例如通信规模缩减和客户端异构性。前者可缓解通信开销，后者则允许客户端根据其可用计算资源选择合适的模型。为应对这些挑战，本文采用基于神经ODE的模型进行联邦学习。所提出的灵活联邦学习方法能够在聚合具有不同迭代次数或深度的模型时，减少通信规模。我们的贡献在于通过实验证明，所提出的联邦学习方法能够聚合具有不同迭代次数或深度的模型，并在准确率方面与另一种联邦学习方法进行了比较。此外，结果表明，相较于使用CIFAR-10数据集的基线ResNet模型，我们的方法可将通信规模降低高达92.4%。