May 27, 2024, 4:45 a.m. | Luca Barbieri, Stefano Savazzi, Sanaz Kianoush, Monica Nicoli, Luigi Serio

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.08087v2 Announce Type: replace-cross
Abstract: Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions. Rather than moving large data volumes from producers (sensors, machines) to energy-hungry data centers, raising environmental concerns due to resource demands, FL provides an alternative solution to mitigate the energy demands of several learning tasks while enabling new Artificial Intelligence of Things (AIoT) applications. …

abstract arxiv carbon communication complexity computational cs.lg data data storage devices edge edge devices eess.sp energy federated learning impact machine machine learning machines moving quantization replace sensors solutions storage tasks technologies terms tracking type

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