Decentralized Machine Learning Whitepaper

Abstract

In the era of rapid development in artificial
intelligence and machine learning, data quality
and relevancy are essential to generate usable
applications of high quality and accuracy for
machine learning.

As compared to publicly-accessible data,
privately-held data are more relevant and
timely for machine learning. These private
data are usually untapped and inaccessible as
they are stored in individual electronic devices
such as smartphones, tablets and computers.
Leading technology firms try to access these
private data when individuals are unaware of
or by providing free service to them in return.
Nevertheless, these leading technology firms
can only obtain a portion of the private data,
which are subset of the massive untapped
private data owned by all individuals.

Decentralized Machine Learning (DML)
protocol is designed to expand the reach
to untapped private data and unleash their
potential to facilitate machine learning
development while providing economic
incentives and protecting data privacy.
Machine learning algorithm will be run on
the devices without extracting the data from
the devices, which will be kept within the
devices. Only the machine learning result
will be aggregated with outcomes generated
from other devices to form an unbiased,
comprehensive and accurate crowdsourced
analytics and predictions. Through DML
protocol, both the private data and processing
power for machine learning are decentralized
as algorithms are run directly on individual
devices by utilizing their idle processing power.

Decentralized Machine LearningWebsite
Decentralized Machine Learning Whitepaper

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