Direct Federated Neural Architecture Search

TL;DR

This paper introduces a novel, efficient, and hardware-agnostic federated neural architecture search method that significantly reduces resource use and improves accuracy for federated learning on heterogeneous data.

Contribution

It presents the first end-to-end, one-stage federated NAS approach that is computationally lightweight and suitable for real-world heterogeneous federated environments.

Findings

Order of magnitude reduction in resource consumption

Outperforms prior NAS methods in accuracy

Suitable for deployment on resource-constrained devices

Abstract

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.