April 16, 2024, 4:45 a.m. | Zihan Chen, Howard H. Yang, Y. C. Tay, Kai Fong Ernest Chong, Tony Q. S. Quek

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.04003v2 Announce Type: replace-cross
Abstract: Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. Currently, the exploration of the interplay between FMs and FL is still in its nascent stage. Naturally, FMs are capable of boosting the performance of FL, and …

abstract advances ai applications applications artificial artificial intelligence arxiv brand cs.dc cs.lg cs.ni cs.sy eess.sy federated learning foundation general generative generative ai applications intelligence key multiple networks next role serve type vision wireless world

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Machine Learning Engineer

@ Samsara | Canada - Remote