  The ability of neural radiance fields or NeRFs to conduct accurate 3D
modelling has motivated application of the technique to scene representation.
Previous approaches have mainly followed a centralised learning paradigm, which
assumes that all training images are available on one compute node for
training. In this paper, we consider training NeRFs in a federated manner,
whereby multiple compute nodes, each having acquired a distinct set of
observations of the overall scene, learn a common NeRF in parallel. This
supports the scenario of cooperatively modelling a scene using multiple agents.
Our contribution is the first federated learning algorithm for NeRF, which
splits the training effort across multiple compute nodes and obviates the need
to pool the images at a central node. A technique based on low-rank
decomposition of NeRF layers is introduced to reduce bandwidth consumption to
transmit the model parameters for aggregation. Transferring compressed models
instead of the raw data also contributes to the privacy of the data collecting
agents.
