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.

研究表明基于分布式的 Federated Learning 在处理数据分布不一致的情况下存在结构不适配的情况，提出基于 Neural Architecture Search 的 Federated NAS 算法来自动化协作搜索模型架构以提高模型精度和减少手动设计工作，在非独立同分布数据集上实验结果表明 FedNAS 所搜索到的模型架构胜过手动预设的模型架构。

利用FedNAS进行非IID和隐形数据的联邦深度学习：神经结构搜索