| Pub. Date | : Feb, 2024 |
|---|---|
| Product Name | : The IUP Journal of Marketing Management |
| Product Type | : Article |
| Product Code | : IJMM020224 |
| Author Name | : K G Sagar |
| Availability | : YES |
| Subject/Domain | : Marketing |
| Download Format | : PDF Format |
| No. of Pages | : 17 |
Federated learning (FL) is an emerging tool involving a decentralized machine learning (ML) paradigm. It shows lots of promise encompassing viable solutions to address data privacy and efficiency in the manufacturing industry. Though quite expensive, FL is a key process wherein input settings, data collection and analysis in the manufacturing sector become important. These aspects hinder the advanced ML and data-driven methods, which needs to be addressed through offline training program. FL plays a crucial role in effectively tackling significant issues in digital manufacturing (DM) and its advanced version additive manufacturing (AM). The paper provides a thorough analysis of the application of FL in the context of DM and AM. It also reviews the current state of the manufacturing industry with a focus on the difficulties and potential advantages associated with contemporary production methodologies.
Real-time data collection, through Internet of Things (IoT), Industry 4.0, and advanced analysis from the cloud, is being adopted by modeling machine learning (ML) sequentially. As regards small-scale manufacturers, they face a lot of challenging issues in respect of not sharing resources of large industries. Nevertheless, they experience problems in attaining the benefits of IoT in view of not generating the required data operational in a private cloud and not being in a position to share the raw data from a public cloud.
Federated learning, Digital manufacturing, Collaborative learning, Distributed learning, Privacy-preserving learning, Machine learning, Artificial intelligence, Additive manufacturing