no code implementations • 24 Apr 2024 • Liang Qu, Cunze Wang, Yuhui Shi
Federated learning, as a privacy-preserving machine learning architecture, has shown promising performance in balancing data privacy and model utility by keeping private data on the client's side and using a central server to coordinate a set of clients for model training through aggregating their uploaded model parameters.