AAAI 2026technical0 citations
FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning
Yichen Li, Hang Su, Huifa Li, Haolin Yang, Xinlin Zhuang, Haochen Xue, Haozhao Wang, Imran Razzak
Abstract
Knowledge Distillation (KD) serves as an effective approach to addressing heterogeneity issues in Federated Learning (FL), leveraging additional datasets to align local and global models better. There are two primary distillation paradigms: feature-based distillation, which utilizes intermediate-layer features of the network, and logit-based distillation, which employs the final layer
BibTeX
@inproceedings{aaai2026_fedcdtowardscons,
title = {FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning},
author = {Yichen Li and Hang Su and Huifa Li and Haolin Yang and Xinlin Zhuang and Haochen Xue and Haozhao Wang and Imran Razzak},
booktitle = {AAAI 2026},
year = {2026}
}