# Industrial Federated Learning -- Requirements and System Design

arXiv: https://arxiv.org/abs/2005.06850 · HTML: https://mlsanity.com/paper/2005.06850/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 6 of 6)

- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [An Efficient and Robust System for Vertically Federated Random Forest](https://mlsanity.com/paper/2201.10761/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [CommunityAI: Towards Community-based Federated Learning](https://mlsanity.com/paper/2311.17958/) (2023)
- [Decision Models for Selecting Federated Learning Architecture Patterns](https://mlsanity.com/paper/2204.13291/) (2022)

## Abstract (arXiv)

Federated Learning (FL) is a very promising approach for improving decentralized Machine Learning (ML) models by exchanging knowledge between participating clients without revealing private data. Nevertheless, FL is still not tailored to the industrial context as strong data similarity is assumed for all FL tasks. This is rarely the case in industrial machine data with variations in machine type, operational- and environmental conditions. Therefore, we introduce an Industrial Federated Learning (IFL) system supporting knowledge exchange in continuously evaluated and updated FL cohorts of learning tasks with sufficient data similarity. This enables optimal collaboration of business partners in common ML problems, prevents negative knowledge transfer, and ensures resource optimization of involved edge devices.