FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data

Researchers propose FedLECC, a new client selection strategy for federated learning that improves AI model training efficiency in distributed environments. The method groups clients by data similarity and prioritizes those with higher loss, achieving up to 12% better accuracy while reducing communication overhead by 50%.