Gestational weight gain prediction using privacy preserving federated learning.

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data. Federated learning can help mitigate this problem by sending local copies of trained models instead of raw data and aggregate them at the central server. In this paper, we present a privacy preserving federated learning approach where the participating users collaboratively learn and update the global model. Furthermore, we show that this model updation can be done incrementally without having the need to store the local updates eternally. Our proposed model achieves a mean absolute error of 4.455 kgs whilst preserving privacy against 2.572 kgs achieved in a centralised approach utilising individual training data until day 140.Clinical relevance- Privacy preserving training of machine learning algorithm for early gestational weight gain prediction with minor tradeoff to performance.

Trusted Operations on Sensor Data †.

The widespread use of mobile devices has allowed the development of participatory sensing systems that capture various types of data using the existing or external sensors attached to mobile devices. Gathering data from such anonymous sources requires a mechanism to establish the integrity of sensor readings. In many cases, sensor data need to be preprocessed on the device itself before being uploaded to the target server while ensuring the chain of trust from capture to the delivery of the data. This can be achieved by a framework that provides a means to implement arbitrary operations to be performed on trusted sensor data, while guaranteeing the security and integrity of the data. This paper presents the design and implementation of a framework that allows the capture of trusted sensor data from both external and internal sensors on a mobile phone along with the development of trusted operations on sensor data while providing a mechanism for performing predefined operations on the data such that the chain of trust is maintained. The evaluation shows that the proposed system ensures the security and integrity of sensor data with minimal performance overhead.