Direct Federated Neural Architecture Search
Anubhav Garg, Amit Kumar Saha, Debo Dutta

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,…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Advanced Neural Network Applications · Adversarial Robustness in Machine Learning
