Distributed Learning with Memory: Optimizing Model Usage across Training Tasks
Paper i proceeding, 2024

As the relevance of distributed learning to vehicular services grows, it becomes more important to perform such learning in the most effective possible manner. In this paper, we investigate the benefits stemming from a learning controller considering multiple learning tasks at the same time. Our performance evaluation shows that this new paradigm, which also enables model reusage across learning tasks, yields up to 60% savings on model training costs.

Författare

F. Malandrino

Consiglio Nazionale delle Ricerche (CNR)

Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)

Carla Fabiana Chiasserini

Nätverk och System

2024 22ND MEDITERRANEAN COMMUNICATION AND COMPUTER NETWORKING CONFERENCE, MEDCOMNET 2024


9798350390476 (ISBN)

22nd Mediterranean Communication and Computer Networking Conference (MedComNet)
Nice, France,

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1109/MEDCOMNET62012.2024.10578134

Mer information

Senast uppdaterat

2024-10-18