IEEE TNNLS

FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification

FedSKD: 一种无需聚合的联邦学习模型异质性方法通过多维相似性知识蒸馏用于医学图像分类

Ziqiao Weng, Weidong Cai, Bo Zhou

AI总结 FedSKD通过多维相似性知识蒸馏实现无需聚合的异构联邦学习，提升医学图像分类的个性化与泛化能力。

Comments Accepted at IEEE-TNNLS, 17 pages

GeNeX: Genetic Network eXperts framework for addressing Validation Overfitting

GeNeX：用于解决验证过拟合的遗传网络专家框架

Emmanuel Pintelas, Ioannis E. Livieris

AI总结 GeNeX通过遗传算法和双路径策略缓解验证过拟合，生成鲁棒模型并构建互补集成，提升模型泛化能力。

Comments Accepted for publication in IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

Journal ref IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2026