Data-Aware Device Scheduling for Federated Edge Learning. (arXiv:2102.09491v2 [cs.DC] UPDATED)

Web: http://arxiv.org/abs/2102.09491

Federated Edge Learning (FEEL) involves the collaborative training of machine
learning models among edge devices, with the orchestration of a server in a
wireless edge network. Due to frequent model updates, FEEL needs to be adapted
to the limited communication bandwidth, scarce energy of edge devices, and the
statistical heterogeneity of edge devices' data distributions. Therefore, a
careful scheduling of a subset of devices for training and uploading models is
necessary. In contrast to previous work in FEEL where the …

arxiv data edge learning scheduling