Tag: client selection

Researchers propose the ASSFL protocol, an adaptive semi-synchronous federated learning method that uses a semi-decentralized topology to improve accuracy and ...

A new decentralized federated learning framework uses particle swarm optimization to dynamically select clients, reporting 76.2% accuracy on CIFAR-10 under ...

A particle swarm optimization strategy that dynamically selects the most informative clients in federated learning matches state-of-the-art methods for medical ...

Researchers at Jerash University have developed an energy-aware federated deep learning framework that cuts per-round energy use by about 43 ...

A new phased client selection method for federated learning improves accuracy, cuts training time and communication costs, and strengthens defenses ...

A new reinforcement learning framework called FLASH-DRM selects the most valuable devices for each federated learning round, improving accuracy, latency ...