We study few-shot Natural Language Understanding (NLU) tasks with Large Language
Models (LLMs) in federated learning (FL) scenarios, which is challenging due to limited data …

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning
model without exchanging their own local data. In this way, the server can exploit the …

Existing federated learning paradigms usually extensively exchange distributed models at a
central solver to achieve a more powerful model. However, this would incur severe …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …

We study a federated learning (FL) framework to effectively train models from scarce and
skewly distributed labeled data. We consider a challenging yet practical scenario: a few data …

Natural language processing (NLP) sees rich mobile applications. To support various
language understanding tasks, a foundation NLP model is often fine-tuned in a federated …

Personalized federated learning (PFL) learns a personalized model for each client in a
decentralized manner, where each client owns private data that are not shared and data …

We investigate how to enhance answer precision in frequently asked questions posed by
distributed users using cloud-based Large Language Models (LLMs). Our study focuses on …

Targeting to produce new features from limited data, few-shot feature generation
approaches have attracted extensive attention and successfully mitigated the high cost of …

Image classification using convolutional neural networks (CNNs) is critical for broader
industrial applications like defect detection. To protect sensitive data during the industrial …