Federated learning (FL) has emerged as a promising approach to training machine learning models across decentralized data sources while preserving data privacy, particularly in manufacturing and shared production environments. However, the presence of data heterogeneity variations in data distribution, quality, and volume across different or clients and production sites, poses significant challenges to the effectiveness and efficiency of FL. This paper provides a comprehensive overview of heterogeneity in FL within the context of manufacturing, detailing the types and sources of heterogeneity, including non-independent and identically distributed (non-IID) data, unbalanced data, variable data quality, and statistical heterogeneity. We discuss the impact of these types of heterogeneity on model training and review current methodologies for mitigating their adverse effects. These methodologies include personalized and customized models, robust aggregation techniques, and client selection techniques. By synthesizing existing research and proposing new strategies, this paper aims to provide insight for effectively managing data heterogeneity in FL, enhancing model robustness, and ensuring fair and efficient training across diverse environments. Future research directions are also identified, highlighting the need for adaptive and scalable solutions to further improve the FL paradigm in the context of Industry 4.0.

本研究针对联邦学习中的数据异质性问题，提出了相应的解决方案，以提高模型在制造和共享生产环境中的有效性。文章系统地分析了异质性的类型及其对模型训练的影响，并提出个性化建模、稳健聚合技术等新策略，以增强模型的鲁棒性和训练的公平性，推动工业4.0背景下的联邦学习发展。

解决联邦学习中的异质性问题：共享生产环境的挑战与解决方案