# Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions

arXiv: https://arxiv.org/abs/2302.13514 · HTML: https://mlsanity.com/paper/2302.13514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Survey of Personalization Techniques for Federated Learning](https://mlsanity.com/paper/2003.08673/) (2020)
- [Split Learning for collaborative deep learning in healthcare](https://mlsanity.com/paper/1912.12115/) (2019)
- [Energy Demand Prediction with Federated Learning for Electric Vehicle Networks](https://mlsanity.com/paper/1909.00907/) (2019)
- [Application of federated learning in manufacturing](https://mlsanity.com/paper/2208.04664/) (2022)
- [Federated Learning Versus Classical Machine Learning: A Convergence Comparison](https://mlsanity.com/paper/2107.10976/) (2021)
- [Guiding the Sequential Experiments in Autonomous Experimentation Platforms through EI-based Bayesian Optimization and Bayesian Model Averaging](https://mlsanity.com/paper/2302.13360/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset](https://mlsanity.com/paper/2303.09703/) (2023)

## Abstract (arXiv)

In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. It also hinders the use of advanced machine learning and data-driven methods which require a substantial amount of offline training data to generate good results. It is particularly challenging for small manufacturers who do not share the resources of a large enterprise. Recently, with the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time, sent to the cloud for advanced analysis, and used to update the machine learning model sequentially. Nevertheless, small manufacturers face two obstacles in reaping the benefits of IoT: they may be unable to afford or generate enough data to operate a private cloud, and they may be hesitant to share their raw data with a public cloud. Federated learning (FL) is an emerging concept of collaborative learning that can help small-scale industries address these issues and learn from each other without sacrificing their privacy. It can bring together diverse and geographically dispersed manufacturers under the same analytics umbrella to create a win-win situation. However, the widespread adoption of FL across multiple manufacturing organizations remains a significant challenge. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry, with a specific emphasis on the perspectives of Industry 4.0 and 5.0.