A Comprehensive Comparison of Federated Learning Frameworks

Kurzfassung

Federated Learning is a Machine Learning paradigm that allows institutions to train models on distributed data while preserving data privacy. The selection of a suitable framework is a crucial step in designing a Federated Learning System, as the choice can influence the capabilities and limitations of the system. While previous research has mainly focused on comparing Federated Learning functionalities, other important criteria, such as usability, technical performance, and legal aspects have often been addressed only partially or overlooked entirely. To address this gap and support informed framework selection, we conducted a comprehensive comparison of Federated Learning frameworks. Frameworks were identified through a literature review and an analysis of GitHub repositories. The comparison considers five relevant comparison criteria: (1) Federated Learning functionalities, (2) user-friendliness, (3) technical aspects, (4) legal aspects and (5) performance evaluation.