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Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression
[article]
2021
arXiv
pre-print
A fundamental issue for federated learning (FL) is how to achieve optimal model performance under highly dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge FL server via multiple communication channels (e.g., 4G, LTE and 5G). However, having an edge device send copies of local models to the FL server along multiple channels is redundant, time-consuming, and would waste resources (e.g., bandwidth, battery life and
arXiv:2109.08819v1
fatcat:trcwuca34rdcdltkxprmx6wfca