Experiments of Federated Learning on Raspberry Pi Boards

Sammanfattning

In recent years, companies of all sizes have become increasingly dependent on customer user data and processing it using machine learning (ML) methods. These methods do, however, require the raw user data to be stored locally on a server or cloud service, raising privacy concerns. Hence, the purpose of this paper is to analyze a new alternative ML method, called federated learning (FL). FL allows the data to remain on each respective device while still being able to create a global model by averaging local models on each client device. The analysis in this report is based on two different types of simulations. The first is simulations in a virtual environment where a larger number of devices can be included, while the second is simulations on a physical testbed of Raspberry Pi (RPI) single-board computers. Different parameters are changed and altered to find the optimal performance, accuracy, and loss of computations in each case. The results of all simulations show that fewer clients and more training epochs increase the accuracy when using independent and identically distributed (IID) data. However, when using non-IID data, the accuracy is not dependent on the number of epochs, and it becomes chaotic when decreasing the number of clients which are sampled each round. Furthermore, the tests on the RPIs show results which agree with the virtual simulation.