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Centaur: Federated Learning for Constrained Edge Devices. (arXiv:2211.04175v1 [cs.LG])
Nov. 9, 2022, 2:11 a.m. | Fan Mo, Mohammad Malekzadeh, Soumyajit Chatterjee, Fahim Kawsar, Akhil Mathur
cs.LG updates on arXiv.org arxiv.org
Federated learning (FL) on deep neural networks facilitates new applications
at the edge, especially for wearable and Internet-of-Thing devices. Such
devices capture a large and diverse amount of data, but they have memory,
compute, power, and connectivity constraints which hinder their participation
in FL. We propose Centaur, a multitier FL framework, enabling ultra-constrained
devices to efficiently participate in FL on large neural nets. Centaur combines
two major ideas: (i) a data selection scheme to choose a portion of samples
that …
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