  Recently, some Neural Architecture Search (NAS) techniques are proposed for
the automatic design of Graph Convolutional Network (GCN) architectures. They
bring great convenience to the use of GCN, but could hardly apply to the
Federated Learning (FL) scenarios with distributed and private datasets, which
limit their applications. Moreover, they need to train many candidate GCN
models from scratch, which is inefficient for FL. To address these challenges,
we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios.
FL-AGCNS designs a federated evolutionary optimization strategy to enable
distributed agents to cooperatively design powerful GCN models while keeping
personal information on local devices. Besides, it applies the GCN SuperNet and
a weight sharing strategy to speed up the evaluation of GCN models.
Experimental results show that FL-AGCNS can find better GCN models in short
time under the FL framework, surpassing the state-of-the-arts NAS methods and
GCN models.
