  Neural Architecture Search (NAS) is a collection of methods to craft the way
neural networks are built. We apply this idea to Federated Learning (FL),
wherein predefined neural network models are trained on the client/device data.
This approach is not optimal as the model developers can't observe the local
data, and hence, are unable to build highly accurate and efficient models. NAS
is promising for FL which can search for global and personalized models
automatically for the non-IID data. Most NAS methods are computationally
expensive and require fine-tuning after the search, making it a two-stage
complex process with possible human intervention. Thus there is a need for
end-to-end NAS which can run on the heterogeneous data and resource
distribution typically seen in the FL scenario. In this paper, we present an
effective approach for direct federated NAS which is hardware agnostic,
computationally lightweight, and a one-stage method to search for
ready-to-deploy neural network models. Our results show an order of magnitude
reduction in resource consumption while edging out prior art in accuracy. This
opens up a window of opportunity to create optimized and computationally
efficient federated learning systems.
