Personalized Wireless Federated Learning for Large Language Models

arXiv:2404.13238v1 Announce Type: new
Abstract: Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their deployment in wireless networks still face challenges, i.e., a lack of privacy and security protection mechanisms. Federated Learning (FL) has emerged as a promising approach to address these challenges. Yet, it suffers from issues including inefficient handling with big and heterogeneous data, resource-intensive training, and high communication overhead. To tackle these issues, we first compare different learning stages and their features of LLMs …

abstract arxiv challenges cs.ai cs.cl cs.lg deployment face federated learning however language language models language processing large language large language models llms natural natural language natural language processing networks personalized privacy privacy and security processing protection security tasks type wireless