Federated learning offers a new foundation for AI — one where privacy, transparency and innovation can move together.

To address these challenges, we propose a Noise-Consistent hypeRgraph AutoEncoder framework with denoising strategies, termed NCRAE, aimed at achieving robust node embeddings in ceRNA regulatory ...

Federated Learning in Sensitive and Regulated Domains FL can operate within government, defense, health care and utility networks, enabling private data to remain local while still contributing to ...

FedSC: Federated Learning with Semantic-Aware Collaboration This is an official implementation of the following paper: Huan Wang, Haoran Li, Huaming Chen, Jun Yan, Jiahua Shi, Jun Shen. "FedSC: ...

Federated learning allows agencies to train AI models without giving up control of their raw data.

In addition, federated learning can enable collaborative, privacy-preserving model training across distributed healthcare systems, so sensitive patient data remains secure and is used for the benefit ...

Federated learning (FL) is a machine learning technique that enables training machine learning models across multiple decentralized edge devices (e.g., smart phones and web browsers) or data silos ...

Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration ...

This paper proposes a novel framework that integrates federated learning with edge AI to overcome existing limitations and pave the way for the next generation of decentralized AI systems. The key ...