  Federated learning (FL) brings collaborative intelligence into industries
without centralized training data to accelerate the process of Industry 4.0 on
the edge computing level. FL solves the dilemma in which enterprises wish to
make the use of data intelligence with security concerns. To accelerate
industrial Internet of things with the further leverage of FL, existing
achievements on FL are developed from three aspects: 1) define terminologies
and elaborate a general framework of FL for accommodating various scenarios; 2)
discuss the state-of-the-art of FL on fundamental researches including data
partitioning, privacy preservation, model optimization, local model
transportation, personalization, motivation mechanism, platform & tools, and
benchmark; 3) discuss the impacts of FL from the economic perspective. To
attract more attention from industrial academia and practice, a FL-transformed
manufacturing paradigm is presented, and future research directions of FL are
given and possible immediate applications in Industry 4.0 domain are also
proposed.
