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Industry 4.0 operates based on IoT devices, sensors, and actuators,transforming the use of computing resources and software solutions in diversesectors. Various Industry 4.0 latency-sensitive applications function based onmachine learning to process sensor data for automation and other industrialactivities. Sending sensor data to cloud systems is time consuming anddetrimental to the latency constraints of the applications, thus, fog computingis often deployed. Executing these applications across heterogeneous fogsystems demonstrates stochastic execution time behavior that affects the taskcompletion time. We investigate and model various Industry 4.0 ML-basedapplications' stochastic executions and analyze them. Industries like oil andgas are prone to disasters requiring coordination of various latency-sensitiveactivities. Hence, fog computing resources can get oversubscribed due to thesurge in the computing demands during a disaster. We propose federating nearbyfog computing systems and forming a fog federation to make remote Industry 4.0sites resilient against the surge in computing demands. We propose astatistical resource allocation method across fog federation forlatency-sensitive tasks. Many of the modern Industry 4.0 applications operatebased on a workflow of micro-services that are used alone within an industrialsite. As such, industry 4.0 solutions need to be aware of applications'architecture, particularly monolithic vs. micro-service. Therefore, we proposea probability-based resource allocation method that can partition micro-serviceworkflows across fog federation to meet their latency constraints. Anotherconcern in Industry 4.0 is the data privacy of the federated fog. As such, wepropose a solution based on federated learning to train industrial MLapplications across federated fog systems without compromising the dataconfidentiality.

Federated Fog Computing for Remote Industry 4.0 Applications