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

TL;DR

This paper proposes a multi-level federated learning approach that addresses class imbalance and worker selection to improve model robustness in remote Industry 4.0 applications, demonstrating a 3-5% performance gain over baseline methods.

Contribution

It introduces a novel combination of loss functions and dynamic worker selection mechanisms to handle class imbalance and enhance federated learning robustness in remote industrial settings.

Findings

Achieved 3-5% performance improvement over baseline federated learning methods.

Effectively mitigated class imbalance issues in local datasets.

Enhanced global model robustness through dynamic worker selection.

Abstract

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.