Reproduce “A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Jiehan Zhou, Shouhua Zhang, Qinghua Lu, Wenbin Dai, Min Chen, Xin Liu, Susanna Pirttikangas, Yang Shi and 2 more
This survey provides an in-depth review of Federated Learning (FL), specifically bridging the gap between its theoretical advancements and practical applications for accelerating Industry 4.0 and the Industrial Internet of Things (IIoT). It categorizes fundamental FL research, proposes a manufacturing paradigm, and outlines immediate industrial application opportunities alongside future research directions.
Key insights
- Despite significant growth in FL research, there is a notable gap in studies specifically addressing Industry 4.0 and smart manufacturing applications.
- Existing FL solutions are predominantly based on Centralized FL (CFL) and Horizontal FL (HFL), with a lack of developed algorithms and applications for Vertical FL (VFL) and Federated Transfer Learning (FTL).
- Key challenges remain in quantifying data privacy exposure, developing universal benchmarks for FL systems, and creating comprehensive FL tool platforms.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.