Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-Shot Federated Learning with Neuromorphic Processors   Being very low power, the use of neuromorphic processors in mobile devices to
solve machine learning problems is a promising alternative to traditional Von
Neumann processors. Federated Learning enables entities such as mobile devices
to collaboratively learn a shared model while keeping their training data
local. Additionally, federated learning is a secure way of learning because
only the model weights need to be shared between models, keeping the data
private. Here we demonstrate the efficacy of federated learning in neuromorphic
processors. Neuromorphic processors benefit from the collaborative learning,
achieving state of the art accuracy on a one-shot learning gesture recognition
task across individual processor models while preserving local data privacy.

5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
18
The categories of this paper are: econ
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph