For modern artificial intelligence (AI) applications such as large language models (LLMs),
the training paradigm has recently shifted to pre-training followed by fine-tuning …

Large Language Models (LLMs) have revolutionized natural language processing tasks.
However, their deployment in wireless networks still face challenges, ie, a lack of privacy …

As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for
data is growing. Much of this data is private and distributed across edge devices, making …

Pre-trained Language Models (PLMs) have shown excellent performance on various
downstream tasks after fine-tuning. Nevertheless, the escalating concerns surrounding user …

In this research paper, we introduce a novel approach to Personalized Federated Learning
(PFL), which we call FedLoRA. This approach is inspired by recent advancements in fine …

Despite the versatility of pre-trained language models (PLMs) across domains, their large
memory footprints pose significant challenges in federated learning (FL), where the training …

Finetuning large language models (LLMs) in federated learning (FL) settings has become
important as it allows resource-constrained devices to finetune a model using private data …

The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering
in a new era in privacy-preserving natural language processing. However, the intensive …

Large language models (LLMs) have demonstrated great capabilities in various natural
language understanding and generation tasks. These pre-trained LLMs can be further …

Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of
Large Language Models (LLMs). While promising, it raises significant challenges due to the …