FedIN: Federated Intermediate Layers Learning for Model Heterogeneity. - Yun-Hin Chan; Zhihan Jiang; Jing Deng; Edith C. H. Ngai (CoRR, 2023)

Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning. - Yun-Hin Chan; Rui Zhou; Running Zhao; Zhihan Jiang; Edith C. H. Ngai (ICLR, 2024)

Synergizing Foundation Models and Federated Learning: A Survey. - Shenghui Li; Fanghua Ye 001; Meng Fang; Jiaxu Zhao 002; Yun-Hin Chan; Edith C. H. Ngai; Thiemo Voigt (CoRR, 2024)

Exploiting features and logits in heterogeneous federated learning. - Yun-Hin Chan; Edith C. H. Ngai (Comput. Networks, 2025)

LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks. - Handi Chen; Rui Zhou; Yun-Hin Chan; Zhihan Jiang; Xianhao Chen; Edith C. H. Ngai (IEEE Trans. Mob. Comput., 2025)