# A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications

arXiv: https://arxiv.org/abs/2409.15802 · HTML: https://mlsanity.com/paper/2409.15802/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Tversky loss function for image segmentation using 3D fully convolutional deep networks](https://mlsanity.com/paper/1706.05721/) (2017)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications](https://mlsanity.com/paper/1907.01132/) (2019)
- [On Safeguarding Privacy and Security in the Framework of Federated Learning](https://mlsanity.com/paper/1909.06512/) (2019)
- [Experiments of Federated Learning for COVID-19 Chest X-ray Images](https://mlsanity.com/paper/2007.05592/) (2020)
- [An Object Detection based Solver for Google's Image reCAPTCHA v2](https://mlsanity.com/paper/2104.03366/) (2021)
- [Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data](https://mlsanity.com/paper/2012.05622/) (2020)
- [Analyzing the Performance of Smart Industry 4.0 Applications on Cloud Computing Systems](https://mlsanity.com/paper/2012.06054/) (2020)
- [Serverless Edge Computing for Green Oil and Gas Industry](https://mlsanity.com/paper/1905.04460/) (2019)
- [Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions](https://mlsanity.com/paper/2005.11317/) (2020)
- [Resource Allocation of Industry 4.0 Micro-Service Applications across Serverless Fog Federation](https://mlsanity.com/paper/2401.07194/) (2024)
- [F-FDN: Federation of Fog Computing Systems for Low Latency Video Streaming](https://mlsanity.com/paper/1905.04459/) (2019)
- [UMS: Live Migration of Containerized Services across Autonomous Computing Systems](https://mlsanity.com/paper/2309.03168/) (2023)

## Built on by (most cited first, 1 of 1)

- [Neurosymbolic Learning for Advanced Persistent Threat Detection under Extreme Class Imbalance](https://mlsanity.com/paper/2603.00453/) (2026)

## Abstract (arXiv)

Deep neural network (DNN) models are effective solutions for industry 4.0 applications (eg oil spill detection, fire detection, anomaly detection). However, training a DNN network model needs a considerable amount of data collected from various sources and transferred to the central cloud server that can be expensive and sensitive to privacy. For instance, in the remote offshore oil field where network connectivity is vulnerable, a federated fog environment can be a potential computing platform. Hence it is feasible to perform computation within the federation. On the contrary, performing a DNN model training using fog systems poses a security issue that the federated learning (FL) technique can resolve. In this case, the new challenge is the class imbalance problem that can be inherited in local data sets and can degrade the performance of the global model. Therefore, FL training needs to be performed considering the class imbalance problem locally. In addition, an efficient technique to select the relevant worker model needs to be adopted at the global level to increase the robustness of the global model. Accordingly, we utilize one of the suitable loss functions addressing the class imbalance in workers at the local level. In addition, we employ a dynamic threshold mechanism with user-defined worker's weight to efficiently select workers for aggregation that improve the global model's robustness. Finally, we perform an extensive empirical evaluation to explore the benefits of our solution and find up to 3-5% performance improvement than baseline federated learning methods.