The goal of this project is to identify the most promising measurement method and strategy for measuring the thickness of grease and to design a sensor module with electronic read-out and communication module. This project has been split into two parts: a sensor implementation and a communication system. This report describes the design and implementation of the communication module and power supply. The communication module has been designed to work conform a master-slave principle using MODBUS over an RS-485 databus. The used hardware for the communication module is rated to work within a desired 1 to 100m range. Furthermore the master node is connected to the internet to present the data on a small webserver. The power supply design depends on the implementation of the sensor, and contains the use of linear voltage regulators and inverters. The results show that the communication module works properly, whereas the power supply requires some further work.

Federated learning is an upcoming machine learning concept which allows data from multiple sources be usedfor training of classifiers without said data leaving its origin. In certain research cases using highly privatedata, the step of gathering data can be quite tedious. In such cases, federated learning has the potential tovastly speed up the research cycle. However, the question arises whether such a federated framework givessimilar performance compared to a central model with access to all data, in other words: Whether it mightbe worth the hassle of gathering all data anyway due to the performance difference. In this work, we providean extensive set of experiments comparing central and federated models, using multiple classifiers on multipledatasets. Results show that federated learning indeed has the potential to provide similar results, but thatits nature might enable use cases in which challenges with regards to batch effects between different datasetscould become prevalent