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

• URL: http://arxiv.org/abs/2302.13514v2
• Date: Tue, 16 May 2023 14:56:38 GMT
• Title: Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions
• Abstract summary: In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. With the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry.
• Score: 3.8351350496532057
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Application of federated learning in manufacturing [0.0]
  Federate learning was first proposed by Google researchers in 2016 and is used for example in the improvement of Google's keyboard Gboard. This paper examines which other constraints prevail in production and which federated learning approaches can be considered as a result.
  arXiv  Detail & Related papers  (2022-08-09T11:14:16Z)
• Distributed data analytics [8.415530878975751]
  Recommendation systems are a key component of online service providers. Financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, risk-management, and compliance.
  arXiv  Detail & Related papers  (2022-03-26T14:10:51Z)
• Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT [82.33080550378068]
  The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
  arXiv  Detail & Related papers  (2022-02-03T07:12:36Z)
• INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
  We develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
  arXiv  Detail & Related papers  (2021-11-16T18:42:50Z)
• Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions [18.95670953718066]
  Federated Learning (FL) emerges as a sub-field of machine learning.<n>This paper reviews the potential of FL in big data acquisition, storage, big data analytics and further privacy preservation.<n>The potential of FL in big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media are also explored.
  arXiv  Detail & Related papers  (2021-10-08T14:36:43Z)
• Fusion of Federated Learning and Industrial Internet of Things: A Survey [4.810675235074399]
  Industrial Internet of Things (IIoT) lays a new paradigm for the concept of Industry 4.0 and paves an insight for new industrial era. Smart machines and smart factories use machine learning/deep learning based models for incurring intelligence. In order to address this issue, federated learning (FL) technology is implemented in IIoT by the researchers nowadays to provide safe, accurate, robust and unbiased models.
  arXiv  Detail & Related papers  (2021-01-04T06:28:32Z)
• SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation [1.567576360103422]
  High-quality training data from multiple data controllers with privacy preservation is a key challenge to train high-quality machine learning models. Both academia researchers and industrial vendors are strongly motivated to propose two main-stream folders of solutions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short) These two solutions have their advantages and limitations when we evaluate them from privacy preservation, ways of communication, communication overhead, format of data, the accuracy of trained models, and application scenarios.
  arXiv  Detail & Related papers  (2020-12-06T22:24:28Z)
• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [49.367020832638794]
  The aerospace industry is poised to capitalize on big data and machine learning. Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
  arXiv  Detail & Related papers  (2020-08-24T22:40:26Z)
• Evaluating the Communication Efficiency in Federated Learning Algorithms [3.713348568329249]
  Recently, in light of new privacy legislations in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models, without sharing the privacy-sensitive data. This raises the challenge of communication cost when implementing FL at large scale.
  arXiv  Detail & Related papers  (2020-04-06T15:31:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.