Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
Abstract
Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-independent and identically distributed (Non-IID) private data and unevenly distributed computational resources. Preserving user data privacy while optimizing AI/ML models in a heterogeneous federated network requires us to address data and system/resource heterogeneity. To address these challenges, we propose Resource-aware Federated Learning (RaFL). RaFL allocates resource-aware specialized models to edge devices using Neural Architecture Search (NAS) and allows heterogeneous model architecture deployment by knowledge extraction and fusion. Combining NAS and FL enables on-demand customized model deployment for resource-diverse edge devices. Furthermore, we propose a multi-model architecture fusion scheme allowing the aggregation of the distributed learning results. Results demonstrate RaFL's superior resource efficiency compared to SoTA.
Cite
@article{arxiv.2211.05716,
title = {Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search},
author = {Sixing Yu and J. Pablo Muñoz and Ali Jannesari},
journal= {arXiv preprint arXiv:2211.05716},
year = {2024}
}
Comments
Accepted at the 30th International European Conference on Parallel and Distributed Computing (Euro-Par 2024)