2025-01-14-xie25b.md

title	FedDGL: Federated Dynamic Graph Learning for Temporal Evolution and Data Heterogeneity
booktitle	Proceedings of the 16th Asian Conference on Machine Learning
year	2025
volume	260
series	Proceedings of Machine Learning Research
month	0
publisher	PMLR
pdf	https://raw.githubusercontent.com/mlresearch/v260/main/assets/xie25b/xie25b.pdf
url	https://proceedings.mlr.press/v260/xie25b.html
software	https://github.com/zach82/FedDGL
openreview	d00Nfh8akB
abstract	Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution , and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios.
layout	inproceedings
issn	2640-3498
id	xie25b
tex_title	{FedDGL}: {F}ederated Dynamic Graph Learning for Temporal Evolution and Data Heterogeneity
firstpage	463
lastpage	478
page	463-478
order	463
cycles	false
bibtex_editor	Nguyen, Vu and Lin, Hsuan-Tien
editor	given family Vu Nguyen given family Hsuan-Tien Lin
bibtex_author	Xie, Zaipeng and Likun, Li and Chen, Xiangbin and Yu, Hao and Huang, Qian
author	given family Zaipeng Xie given family Li Likun given family Xiangbin Chen given family Hao Yu given family Qian Huang
date	2025-01-14
address
container-title	Proceedings of the 16th Asian Conference on Machine Learning
genre	inproceedings
issued	date-parts 2025 1 14
extras