Centaur: Federated Learning for Constrained Edge Devices

arXiv:2211.04175v3 Announce Type: replace
Abstract: Federated learning (FL) 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 …

abstract applications arxiv centaur compute connectivity constraints cs.lg data devices diverse edge edge devices enabling federated learning framework hinder internet memory power the edge type wearable