Researchers from Sun Yat-sen University’s Shenzhen Campus, led by WenYuan Yang and Gege Jianga, have developed a decentralized federated learning framework, DFUN-KDF, to enhance UAV network efficiency ...

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

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 ...

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

Public repo for Flotilla, a Scalable, Modular and Resilient Federated Learning Framework for Heterogeneous Resources - dream-lab/flotilla ...

As enterprises struggle to balance AI capabilities against data privacy concerns, federated learning provides the best of both worlds.

This comprehensive survey serves as a valuable resource for researchers and practitioners working in the fields of machine learning, data privacy, and distributed computing. It provides a solid ...

Building federated learning with differential privacy to train and refine machine-learning models with more comprehensive datasets can help exploit the potential of machine learning to its fullest.

Distributed machine learning, and Decentralized Federated Learning in particular, is emerging as an effective solution to cope with the ever-increasing amount of data and the need to process it faster ...

A Framework to Design Efficent Blockchain-Based Decentralized Federated Learning Architectures Abstract: Distributed machine learning, and Decentralized Federated Learning in particular, is emerging ...