Clustered Federated Learning with Hierarchical Knowledge Distillation

This article likely presents a novel approach to federated learning, combining clustering techniques with knowledge distillation to improve model performance and efficiency in distributed environments. The hierarchical aspect suggests a structured approach to knowledge transfer, potentially optimizing communication and computation costs. The use of knowledge distillation implies an attempt to compress and transfer knowledge effectively between different models or clusters.