Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

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 …

This paper reviews the state-of-the-art of language models architectures and strategies for"
complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large …

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 …

Despite the versatility of pre-trained language models (PLMs) across domains, their large
memory footprints pose significant challenges in federated learning (FL), where the training …

Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents
from trustworthy corpora, eg, Wikipedia, to produce specific answers. Very recently, a pre …

Since the advent of Federated Learning (FL), research has applied these methods to natural
language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous …

Federated learning (FL) enables multiple participants to collaboratively train machine
learning models using decentralized data sources, alleviating privacy concerns that arise …

Personalized federated learning (PFL) is designed for scenarios with non-independent and
identically distributed (non-IID) client data. Existing model mixup-based methods, one of the …

Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm.
Traditional FL requires all data owners (aka FL clients) to train the same local model. This …