Alessandro Masci
Federated Learning Meets Model Compression for Image Classification on Memory-Constrained Devices.
Rel. Alessio Sacco, Flavio Esposito, Guido Marchetto. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
In a world flooded with electronic devices and sensors, the necessity of making them cooperate increases on a daily basis. At the same time, it is crucial to guarantee the safety and privacy of the exploited data. Furthermore, many of the devices that help us in our daily activities are compact appliances with constrained computational and storage capabilities. Therefore, it is essential to find new solutions that enhance memory efficiency without compromising accuracy. Another main aspect that characterizes the effectiveness of these devices is their speed in performing inference: the vast amount of daily-generated data demands increasingly faster inference times. Thus, looking for new solutions to merge all these necessities is crucial.
One of the most exploited methodologies for enabling different devices to collaboratively enhance the reliability of artificial intelligence models is Federated Learning (FL)
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