  Federated Learning (FL) has been proved to be an effective learning framework
when data cannot be centralized due to privacy, communication costs, and
regulatory restrictions. When training deep learning models under an FL
setting, people employ the predefined model architecture discovered in the
centralized environment. However, this predefined architecture may not be the
optimal choice because it may not fit data with non-identical and independent
distribution (non-IID). Thus, we advocate automating federated learning
(AutoFL) to improve model accuracy and reduce the manual design effort. We
specifically study AutoFL via Neural Architecture Search (NAS), which can
automate the design process. We propose a Federated NAS (FedNAS) algorithm to
help scattered workers collaboratively searching for a better architecture with
higher accuracy. We also build a system based on FedNAS. Our experiments on
non-IID dataset show that the architecture searched by FedNAS can outperform
the manually predefined architecture.
