  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.
