FedFINFO: A General Full-Informativeness Federated Graph Learning from Open Cross-Domain Data

Abstract

Open cross-domain federated graph learning facilitates collaborative learning among clients from distinct graph domains while preserving privacy. However, severe structure and feature heterogeneity in open scenarios exacerbates the multiplicative amplification of structural and feature noises within graph neural network encoders, leading to significant negative transfer. Existing methods addressing partial heterogeneity are insufficient. We theoretically reveal this amplification mechanism and propose a novel learning method FedFINFO, which leverages full-informativeness shared knowledge. Specifically, we construct an aligned and general feature space integrating node semantic and structural roles. We proposed a counterfactual multi-view framework to explicitly learn a structure masker for extracting consensus subgraphs. These jointly suppress the mutual amplification of noises at the source. We also propose a history-aware adaptive multi-channel aggregation mechanism to enable dynamic and personalized sharing driven by channel similarity while preventing collaborative oscillation. FedFINFO effectively reconciles common knowledge sharing with personalized knowledge retention, significantly mitigating negative transfer. Extensive experiments under cross-dataset and cross-domain settings demonstrate the superiority of our method.