HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning

Guancheng Wan, Xiaoran Shang, Yuxin Wu, Guibin Zhang, Jinhe Bi, Liangtao Zheng, Xin Lin, Yue Liu, Yanbiao Ma, Wenke Huang, Bo Du

Wuhan University· NUS· LMU· Renmin University of China· UC San Diego

Federated Learning Graph Learning Robustness Geometric Learning

⋅ NeurIPS ⋅ Poster ⋅OpenReview

Abstract

Robust Federated Graph Learning (FGL) provides an effective decentralized framework for training Graph Neural Networks (GNNs) in noisy-label environments. However, the subtlety of noise during training presents formidable obstacles for developing robust FGL systems.

Previous robust FL approaches neither adequately constrain edge-mediated error propagation nor account for intra-class topological differences. At the client level, we innovatively demonstrate that hyperspherical embedding can effectively capture graph structures in a fine-grained manner.

Correspondingly, our method effectively addresses the aforementioned issues through fine-grained hypersphere alignment. Moreover, we uncover undetected noise arising from localized perspective constraints and propose the geometric-aware hyperspherical purification module at the server level.

Combining both level strategies, we present our robust FGL framework, HYPERION, which operates all components within a unified hyperspherical space. HYPERION demonstrates remarkable robustness across multiple datasets, for instance, achieving a 29.7%

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F1-macro score with 50%-pair noise on Cora. The code is available for anonymous access at \url{https://anonymous.4open.science/r/Hyperion-NeurIPS/}.