Due to the rise of new information and communication technologies manufacturing companies have accessto huge amounts of power consumption data which are measured by sensors and processed by informationsystems. One of the most promising applications of extracting value out of the collected data is the detectionof anomalies in process data from industrial machines and equipment. Many research and industry use casesapply machine learning (ML) techniques for anomaly detection. These techniques enable manufacturingcompanies to optimize their manufacturing processes but also to be more energy efficient and therefore havean impact for sustainable manufacturing. Most of the ML applications use central server infrastructures fordata collection from different sources to process and analyse it for further usage. Nevertheless, privacyconcerns and security risks motivate manufacturers to store the collected sensitive data from the productionline locally. Therefore, suppliers of industrial machines (e.g. robots, machine tools) do not have thepossibility, to store and analyse the data in the cloud, where data from all the machines of the supplier indifferent companies could be analysed and used for ML applications. One of the new paradigm shifts in MLis the concept of federated learning (FL) which enables local devices to use ML without sending data to acentral server. This paper introduces an architecture for using the concepts of FL in manufacturing processesenabling machine suppliers to use ML for optimizing machine processes in a collaborative manner.Therefore, the more general federated learning concept is extended for industrial machinery and equipmentusing the industrial communication framework OPC-UA. Our architecture is tested and validated by usingan industrial dataset of different compressors’ power consumption.