The ability to perform computation on devices, such as smartphones, cars, or other nodes present at the Internet of Things leads to constraints regarding bandwidth, storage, and energy, as most of these devices are mobile and operate on batteries. Using their computational power to perform locally machine learning and analytics tasks can enable accurate and real-time predictions at the network edge. A trained machine learning model requires high accuracy towards the prediction outcome, as wrong decisions can lead to negative consequences on the efficient conclusion of applications. Most of the data sensed in these devices are contextual and personal requiring privacy-preserving without their distribution over the network. When working with these privacy-preserving data, not only the protection is important but, also, the model needs the ability to adapt to regular occurring concept drifts and data distribution changes to guarantee a high accuracy of the prediction outcome. We address the importance of personalization and generalization in edge devices to adapt to data distribution updates over continuously evolving environments. The methodology we propose relies on the principles of Federated Learning and Optimal Stopping Theory extended with a personalization component. The privacy-efficient and quality-awareness of personalization and generalization is the overarching aim of this work.
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