Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search

TL;DR

This paper introduces FedNAS, a neural architecture search method for federated learning that automatically finds better model architectures for non-IID data, improving accuracy over predefined models.

Contribution

The paper proposes FedNAS, a novel federated NAS algorithm that automates architecture search in FL, addressing non-IID data challenges and outperforming manual designs.

Findings

FedNAS outperforms predefined architectures on non-IID datasets.

Automated architecture search improves model accuracy in federated settings.

System implementation demonstrates practical viability of FedNAS.

Abstract

Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When training deep learning models under an FL setting, people employ the predefined model architecture discovered in the centralized environment. However, this predefined architecture may not be the optimal choice because it may not fit data with non-identical and independent distribution (non-IID). Thus, we advocate automating federated learning (AutoFL) to improve model accuracy and reduce the manual design effort. We specifically study AutoFL via Neural Architecture Search (NAS), which can automate the design process. We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy. We also build a system based on FedNAS. Our experiments on non-IID dataset show that the architecture searched by FedNAS can outperform the manually predefined architecture.