The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

The scalability of large language models (LLMs) in handling high-complexity models and
large-scale datasets has led to tremendous successes in pivotal domains. While there is an …

Aligning large language models (LLMs) with human values is imperative to mitigate
potential adverse effects resulting from their misuse. Drawing from the sociological insight …

The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI,
with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning …

Federated learning (FL) enables multiple parties to collaboratively fine-tune an large
language model (LLM) without the need of direct data sharing. Ideally, by training on …

Large Language Models (LLMs) excel in natural language understanding by capturing
hidden semantics in vector space. This process enriches the value of text embeddings for …

Unlike natural language processing and computer vision, the development of Foundation
Models (FMs) for time series forecasting is blocked due to data scarcity. While recent efforts …

Federated learning has enabled multiple parties to collaboratively train large language
models without directly sharing their data (FedLLM). Following this training paradigm, the …

Federated learning (FL) allows multiple devices to train a model collaboratively without
sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning …