The emergence of Industry 4.0 as a de facto paradigm for the industries of the future still presents some needed innovation and problem solving. In a increasing interconnected industrial setup where all nodes in a industrial process are increasingly interconnected, new challenges arise from the need to gather and explain machine learning models obtained from data streams present in multiple locations. These problems may be tackled with the fields of explainable artificial intelligence (xAI) methods and federated learning. The data constraints in temporal dependencies as is the case with most industrial process, may also make these problems fall under the timeseries category. This article presents an initial approach to deal with these problems and provides a theoretical overview of a federated xAI system for timeseries applications in Industry 4.0. The main objective is to present and discuss the integration of previous efforts into a large scale application for machine learning algorithm within I4.0 that offer human understandable decisions for I4.0 decision makers.