Federated Learning with Swift: An Extension of Flower and Performance Evaluation

Maximilian Kapsecker, Daniel N. Nugraha, Christoph Weinhuber, Nicholas D. Lane, Stephan M. Jonas

Abstract: Federated learning (FL) enables the optimization of machine learning models on distributed clients without sharing local data. The integration of FL into a mobile environment is becoming more feasible due to increasing on-device processing capabilities. However, there is limited open-source support for the iOS platform. The article introduces a Swift-based client implementation of the user-friendly FL framework Flower. The objective is facilitating FL client processes based on a modular and easy-to-integrate software development kit. A benchmark test demonstrates consistent stability and performance using the software, further motivating its use for research.