Abstract
Currently, the execution of machine learning algorithms is typically batch, offline and centralized. Managing networks and their services requires running massively distributed, real-time data. In several situations, the real-time validity of the generated data is limited, demanding the reduction of latency in communication and processing. Furthermore, data transmission in a distributed e…