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Internet of Things (IoT) is on the verge of a major paradigm
shift. In the IoT system of the future, IoFT, the “cloud” will
be substituted by the “crowd” where model training is brought to
the edge, allowing IoT devices to collaboratively extract
knowledge and build smart analytics/models while keeping their
personal data stored locally. This paradigm shift was set into
motion by the tremendous increase in computational power on IoT
devices and the recent advances in decentralized and
privacy-preserving model training, coined as federated learning
(FL).
This article provides a vision for IoFT and a systematic
overview of current efforts towards realizing this vision.
Specifically, we first introduce the defining characteristics of
IoFT and discuss FL data-driven approaches, opportunities, and
challenges that allow decentralized inference within three
dimensions: (i)
a global model that maximizes utility across all IoT devices,
(ii) a
personalized model that borrows strengths across all devices yet
retains its own model,
(iii) a
meta-learning model that quickly adapts to new devices or
learning tasks.
We end by
describing the vision and challenges of IoFT in reshaping
different industries through the lens of domain experts. Those
industries include manufacturing, transportation, energy,
healthcare, quality & reliability, business, and computing. Read
the proposed vision for the Internet of Things (IoT) at
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