Sleep is a vital aspect of our life. Having a good quality sleep is necessary for our well-being and health. Therefore, sleep measurements can aid us in improving our sleep quality. While many users are reluctant to use intrusive sleep sensing techniques such as wearables, passive sensing such as network activity of smart phone devices can be utilized to measure the sleep duration of a user. However, to develop accurate sleep prediction models, we need large amounts of labeled data. In addition, due to heterogeneity in user behaviors, hardware and software of the devices used, a single model may not generalise to every user in a given population. Although ground truth data collection from a large population is costly and challenging, unlabelled network activity data is easy to gather using mobile applications or network logs. This motivates us to look for semi-supervised learning approaches to leverage unlabelled data from the users to develop accurate sleep prediction models. Our results show that semi-supervised learning techniques can be used to improve the accuracy of sleep duration estimation from smart devices.