Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search

arXiv:2211.05716v2 Announce Type: replace
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 …

abstract architecture arxiv computational cs.cv cs.lg data data privacy devices distributed edge edge devices federated learning independent ml models neural architecture search privacy private data resources search systems train train ai type user data while