Parameter-efficient fine-tuning (PEFT) methods typically assume that Large Language
Models (LLMs) are trained on data from a single device or client. However, real-world …

Fine-tuning large language models (LLMs) has achieved remarkable performance across
various natural language processing tasks, yet it demands more and more memory as …

Physics-informed neural networks (PINNs) have shown promise in solving partial differential
equations (PDEs), with growing interest in their energy-efficient, real-time training on edge …

Transformer models have achieved state-of-the-art performance across a wide range of
machine learning tasks. There is growing interest in training transformers on resource …

Large language models (LLMs) are revolutionizing many science and engineering fields.
However, their huge model sizes impose extremely demanding needs of computational …

Le equazioni differenziali alle derivate parziali (EDP) sono fondamentali per modellare e
comprendere fenomeni complessi in diverse discipline scientifiche e ingegneristiche, dalla …