Tag
The paper proposes CRAD, a class-wise reliability-aware distillation method for decentralized federated learning to handle heterogeneous architectures and non-IID data, achieving improved accuracy on image classification benchmarks.
Proposes ReTAMamba, a method using reliability-aware temporal aggregation with Mamba for irregular clinical time series prediction, achieving significant AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.