  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.
