We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions.
Poster: Continual Network Learning / Di Cicco N.; Al Sadi A.; Grasselli C.; Melis A.; Antichi G.; Tornatore M.. - ELETTRONICO. - (2023), pp. 1096-1098. (Intervento presentato al convegno ACM SIGCOMM 2023 tenutosi a Columbia University in New York City, US nel 10 -14 / September 2023) [10.1145/3603269.3610855].
Poster: Continual Network Learning
Al Sadi A.;Grasselli C.;Melis A.;
2023
Abstract
We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
