A Federated Learning Framework for IoT: Application to Industry 4.0
Hamza Safri, Mohamed Mehdi Kandi, Youssef Miloudi, Christophe Bortolaso, Denis Trystram, Frédéric Desprez · 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) · 2022
Predictive maintenance aims to anticipate indus-trial equipment failures in order to allow early scheduling of corrective actions. Such a maintenance approach is based on a detailed analysis that takes into account the technical and contextual characteristics of the target industrial equipment. However, this analysis requires a significant period of time to collect a representative quantity of data to learn a predictive model. Federated learning (FL in short) is a promising approach that allows several participants to build collaboratively a global predictive model. This approach has been widely explored in generic loT applications and large scale architectures. However, the implementation of FL in actual environments requires to consider several issues to adapt to existing loT architectures, including the management/orchestration of the federated tasks and handling the limitations of computational resources. Indeed, most of the current research focus on the aggregation of heavy deep learning algorithms. In this paper, we propose an architecture for FL in the context of loT based on the classical 3-layer architecture standardized by ETSI11https://www.etsi.org/. We consider new features for performing federated tasks (training, aggregation and man-agement of each participant). We also propose a stacking-based aggregation method to build the global model in a cost-efficient way. We evaluate finally the performance and effectiveness of this approach on real use-case scenarios. The comparison with other models trained in a centralized way highlights the benefit of our approach.