A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
It provides a comprehensive overview for researchers and practitioners in industrial academia to integrate FL into IIoT, but it is incremental as it surveys existing work without new experimental results.
This survey tackles the challenge of applying federated learning (FL) to accelerate the Industrial Internet of Things (IIoT) by addressing data privacy and security concerns, presenting a framework, discussing state-of-the-art research, and proposing future directions for Industry 4.0.
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.