Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search

• URL: http://arxiv.org/abs/2211.05716v2
• Date: Wed, 1 May 2024 03:31:12 GMT
• Title: Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
• Authors: Sixing Yu, J. Pablo Muñoz, Ali Jannesari,
• Abstract summary: Federated Learning (FL) is used to train AI/ML models in distributed and privacy-preserving settings. We propose Resource-aware Federated Learning (RaFL) RaFL allocates resource-aware specialized models to edge devices using Neural Architecture Search (NAS)
• Score: 8.184714897613166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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