Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 38 references that are in the map. First posted on arXiv 18 Aug 2024.
Builds on
15 of 38 references.
References · 15
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- 2019Advances and Open Problems in Federated Learningcited by 3,021
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- 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
- 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
- 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
- 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
- 2020TiFL: A Tier-based Federated Learning Systemcited by 95
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2020Deflating Dataset Bias Using Synthetic Data Augmentationcited by 37
3 more
- 2022Application of federated learning in manufacturingcited by 11
- 2021FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximationcited by 10
- 2024Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniquescited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.