  Novel view synthesis (NVS) is an important technology for many AR and VR
applications. The recently proposed Neural Radiance Field (NeRF) approach has
demonstrated superior performance on NVS tasks, and has been applied to other
related fields. However, certain application scenarios with distributed data
storage may pose challenges on acquiring training images for the NeRF approach,
due to strict regulations and privacy concerns. In order to overcome this
challenge, we focus on FedNeRF, a federated learning (FL) based NeRF approach
that utilizes images available at different data owners while preserving data
privacy.
  In this paper, we first construct a resource-rich and functionally diverse
federated learning testbed. Then, we deploy FedNeRF algorithm in such a
practical FL system, and conduct FedNeRF experiments with partial client
selection. It is expected that the studies of the FedNeRF approach presented in
this paper will be helpful to facilitate future applications of NeRF approach
in distributed data storage scenarios.
