Federated Learning Deployments of Industrial Applications on Cloud, Fog, and Edge Resources

Federated Learning (FL) has gained prominence as a method for facilitating collaborative and privacy-preserving model training across multiple heterogeneous devices in recent years. In most approaches, the clients are closely deployed to the data source. However, as FL systems are implemented in the industry, multiple platform options can be considered in the design phase.In this paper, we present a novel approach for deploying FL clients to multiple locations considering a multi-platform strategy with cloud, fog, and edge resources. We provide a FL architecture that integrates mechanisms for building cohorts of similar clients and a client selection algorithm for optimizing the performance of all clients with respect to energy consumption, model performance, and FL completion time.We evaluate seven deployment strategies in three scenarios given a real-world use case from the electronics industry and heterogeneous hardware capabilities. Our results show that our approach can improve model performance by up to 15%, while energy consumption and completion time converge to the relatively best deployment.

Subjects

Cloud Computing

Deployment Strategies

Edge Computing

Federated Learning

Fog Computing

Industrial Use Case

Optimization

Personalized Federated Learning

MLE@TUHH

DDC Class

004: Computer Sciences

Options

Federated Learning Deployments of Industrial Applications on Cloud, Fog, and Edge Resources