  The 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.
