Sannara EK

Recent advancements in sensor technology and mobile computing have significantly enhanced pervasive computing applications, integrating smart devices into our environments to offer diverse user-oriented services. These services are further augmented by Machine Learning (ML) models that are increasingly embedded within devices to leverage local computational power and data. However, adapting ML to a user-centric paradigm—prioritizing personalized and generalized local models while ensuring data privacy—presents challenges. Federated Learning (FL), a meta-learning client-server framework, provides a promising solution by avoiding the need to communicate user data. Conventionally, FL has been adopted as a server-centric approach, aiming to aggregate and optimize a single generalized model while leveraging local devices' computing abilities and data for training. However, addressing the demands of pervasive computing necessitates a shift toward a client-centric paradigm.