In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

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

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Federated learning (FL) is a distributed machine learning process, which allows multiple
nodes to work together to train a shared model without exchanging raw data. It offers several …

Large language models (LLMs) show amazing performance on many domain-specific tasks
after fine-tuning with some appropriate data. However, many domain-specific data are …

The incredible development of federated learning (FL) has benefited various tasks in the
domains of computer vision and natural language processing, and the existing frameworks …

Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …

The rapid expansion of the Internet of Things (IoT) and Edge Computing has presented
challenges for centralized Machine and Deep Learning (ML/DL) methods due to the …

Federated Learning (FL) aims to train high-quality models in collaboration with distributed
clients while not uploading their local data, which attracts increasing attention in both …

Federated learning, a promising distributed machine learning paradigm, has been used in
various Internet of Things (IoT) environments to solve isolated data island issues and protect …