A privacy preserving diagnostic collaboration framework for facial paralysis using federated learning. (November 2022) - Nair, Divya G.; Nair, Jyothisha J.; Jaideep Reddy, K.; Aswartha Narayana, C.V.
Abstract: Most of the machine learning and artificial intelligence applications are data driven. When it comes to sensitive data, maintaining the data privacy principles is a big challenge. Building a machine learning model for classifying sensitive data is discussed in this paper. Focus is given for medical field where the patient data comes under sensitive or private information category. There are restrictions to share patient data for research purposes or collaboration among doctors in different hospitals due to the privacy concerns. In this work, we take facial paralysis as an example and discuss how to build a model for facial paralysis detection. Here, the data used for training the model is face images which implicitly reveals identity of the patient. We analyse how the facial paralysis images from multiple hospitals can be combined together for building an efficient facial paralysis detection system without compromising privacy of patients. Support Vector Machine based federated learning is applied for the purpose. Hospitals are considered as clients which are the data sources where the local training happens and there is a server performing federated averaging. Unlike in traditional federated learning, soft clustering approach is considered at server side and the update to each client is different. The federated averaging algorithm at server takes care of the distribution of data each client holds and customises the update sent to each client. This approach improves the local test accuracy and the convergence speed. To validate the findings, experiments are conducted with MNIST and covid pneumonia datasets as well. Graphical abstract: Highlights: Projects 'Facial Paralysis classification' as an application of federated learning. Introduces soft clustering based selective federated averaging. Proposes a collaborative framework for doctors and researchers. Compares Performance with and without soft clustering on different data distributions.