A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things

TL;DR

This survey reviews federated learning's role in Industry 4.0, covering frameworks, technical advances, economic impacts, and future directions to enhance industrial IoT applications securely and efficiently.

Contribution

It provides a comprehensive overview of federated learning in industrial IoT, including a new manufacturing paradigm and future research directions.

Findings

FL enables secure, decentralized data collaboration in Industry 4.0.

Existing FL research covers data partitioning, privacy, and model optimization.

Proposes a new FL-based manufacturing paradigm for industrial applications.

Abstract

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.