Federated Learning with Dynamic Model Exchange

• Published in: Electronics (Basel) 2022-05, Vol.11 (10), p.1530
• Main Authors: Hilberger, Hannes, Hanke, Sten, Bödenler, Markus
• Format: Article
• Language: English
• Subjects: Algorithms; Blockchain; Data integrity; Datasets; Dynamic models; Electronic health records; Federated learning; Infrastructure; Machine learning; Noise; Privacy; Security; Servers; Source code
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11101530

Large amounts of data are needed to train accurate robust machine learning models, but the acquisition of these data is complicated due to strict regulations. While many business sectors often have unused data silos, researchers face the problem of not being able to obtain a large amount of real-world data. This is especially true in the healthcare sector, since transferring these data is often associated with bureaucratic overhead because of, for example, increased security requirements and privacy laws. Federated Learning should circumvent this problem and allow training to take place directly on the data owner’s side without sending them to a central location such as a server. Currently, there exist several frameworks for this purpose such as TensorFlow Federated, Flower, or PySyft/PyGrid. These frameworks define models for both the server and client since the coordination of the training is performed by a server. Here, we present a practical method that contains a dynamic exchange of the model, so that the model is not statically stored in source code. During this process, the model architecture and training configuration are defined by the researchers and sent to the server, which passes the settings to the clients. In addition, the model is transformed by the data owner to incorporate Differential Privacy. To trace a comparison between central learning and the impact of Differential Privacy, performance and security evaluation experiments were conducted. It was found that Federated Learning can achieve results on par with centralised learning and that the use of Differential Privacy can improve the robustness of the model against Membership Inference Attacks in an honest-but-curious setting.