Large scale language models (LLM) have received significant attention and found diverse
applications across various domains, but their development encounters challenges in real …

Federated learning (FL) is an effective machine learning paradigm where multiple clients
can train models based on heterogeneous data in a decentralized manner without …

As the parameter size of large language models (LLMs) continues to expand, there is an
urgent need to address the scarcity of high-quality data. In response, existing research has …

We present COALA, a vision-centric Federated Learning (FL) platform, and a suite of
benchmarks for practical FL scenarios, which we categorize into three levels: task, data, and …

Federated learning (FL) promotes decentralized training while prioritizing data
confidentiality. However its application on resource-constrained devices is challenging due …

Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …

Federated Learning (FL) enables joint training across distributed clients using their local
data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks …

The accurate identification of protein-nucleotide binding residues is crucial for protein
function annotation and drug discovery. Numerous computational methods have been …

The increasing deployment of multiple deep neural networks (DNNs) on edge devices is
revolutionizing mobile vision applications, spanning autonomous vehicles, augmented …

The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of
Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model …