Gproxy: Communication-Efficient Federated Graph Learning With Efficient Adaptive Proxying

Junyang Wang, Lan Zhang, Mu Yuan, Yihang Cheng, Yunhao Yao, Junhao Wang, Zhonghao Hu, Qian Xu, Bo Yu

Abstract: Federated graph learning (FGL) enables multiple participants with distributed but connected graph data to collaboratively train a model in a privacy-preserving way. However, the high communication cost hinders the adoption of FGL in many resource-limited or delay-sensitive applications. In this work, we focus on reducing the communication cost incurred by the transmission of neighborhood information in FGL. We propose to search for local proxies that can play a substitute role as the external neighbors and develop a novel federated graph learning framework named Gproxy. Gproxy utilizes representation similarity and class correlation to select local proxies for external neighbors. Additionally, we propose to dynamically adjust the proxy strategy according to the changing representation of nodes during the iterative training process. We also design a proxy cache to accelerate the search process by reusing proxy search outcomes for similar external neighbors. Furthermore, we provide a theoretical analysis and show that using a proxy node has a similar influence on training when it is sufficiently similar to the external one. Extensive evaluations show that Gproxy significantly reduces communication cost while maintaining model performance compared to strong baselines.

External IDs:doi:10.1109/tmc.2025.3649513