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Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search

arXiv 2211.05716 ↗First posted 9 Nov 2022cs.LG cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 30 references that are in the map. First posted on arXiv 9 Nov 2022.

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References · 22

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10 more
  1. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  2. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  3. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
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  9. 2021Enabling NAS with Automated Super-Network Generationcited by 11
  10. 2020Federated Neural Architecture Searchcited by 10

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