| dc.description.abstract | The incorporation of meta-learning approaches to
on-device learning for IoT systems has emerged as one of
the effective ways of developing intelligent and never-stopping
devices capable of learning and adapting on their own. This
paper aims to examine existing literature to highlight the
progress, prospects, and potential complications prevalent in
this dynamic field. The paper reviews on specialized hardware
architecture, meta-learning algorithms, and system modularity
that support on-device learning in constrained IoT systems.
This study investigates several existing methods to enhance ondevice learning, like as Federated Learning (FL), Transfer
Learning (TL), and Continual Learning (CL) in relation to
IoT systems by using meta-learning. We also cover the predictive modeling perspectives, performance assessment, and
emerging issues such as privacy, security, and professional
ethics. Thus, this review synthesizes latest research works and
current literature to identify gaps of existing knowledge to
enhance on-device learning in IoT systems through metalearning techniques. This enables researchers and practitioners to get insights with a comprehensive understanding of the
state-of-the-art, future prospects and potential developments
of on-device meta-learning in IoT systems, fostering further
advancements in this rapidly evolving area of study. | en_US |
