Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Energy-Harvesting Distributed Machine Learning   This paper provides a first study of utilizing energy harvesting for
sustainable machine learning in distributed networks. We consider a distributed
learning setup in which a machine learning model is trained over a large number
of devices that can harvest energy from the ambient environment, and develop a
practical learning framework with theoretical convergence guarantees. We
demonstrate through numerical experiments that the proposed framework can
significantly outperform energy-agnostic benchmarks. Our framework is scalable,
requires only local estimation of the energy statistics, and can be applied to
a wide range of distributed training settings, including machine learning in
wireless networks, edge computing, and mobile internet of things.

23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess