Web: http://arxiv.org/abs/2205.05867

May 13, 2022, 1:11 a.m. | Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener

cs.LG updates on arXiv.org arxiv.org

This paper considers over-the-air federated learning (OTA-FL). OTA-FL
exploits the superposition property of the wireless medium, and performs model
aggregation over the air for free. Thus, it can greatly reduce the
communication cost incurred in communicating model updates from the edge
devices. In order to fully utilize this advantage while providing comparable
learning performance to conventional federated learning that presumes model
aggregation via noiseless channels, we consider the joint design of
transmission scaling and the number of local iterations at …

arxiv computation federated learning learning power

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