With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Abstract Cross-device Federated Learning (FL) faces significant challenges where low-end
clients that could potentially make unique contributions are excluded from training large …

Federated learning (FL) is an emerging machine learning paradigm that allows multiple
parties to train a shared model collaboratively in a privacy-preserving manner. Existing …

Transfer learning via fine-tuning pre-trained transformer models has gained significant
success in delivering state-of-the-art results across various NLP tasks. In the absence of …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Although Federated Learning (FL) enables global model training across clients without
compromising their raw data, due to the unevenly distributed data among clients, existing …

In this work, we propose GPT-FL, a generative pre-trained model-assisted federated
learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to …

Abstract In cross-device Federated Learning (FL) environments, scaling synchronous FL
methods is challenging as stragglers hinder the training process. Moreover, the availability …

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …