FedorAS: Federated Architecture Search under system heterogeneity. (arXiv:2206.11239v2 [cs.LG] UPDATED)

Federated learning (FL) has recently gained considerable attention due to its
ability to use decentralised data while preserving privacy. However, it also
poses additional challenges related to the heterogeneity of the participating
devices, both in terms of their computational capabilities and contributed
data. Meanwhile, Neural Architecture Search (NAS) has been successfully used
with centralised datasets, producing state-of-the-art results in constrained
(hardware-aware) and unconstrained settings. However, even the most recent work
laying at the intersection of NAS and FL assumes homogeneous …

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