Aligning Condensed Graph via Hashing: A New Insight for Federated Graph Learning.
Federated Graph Learning (FGL) aims to maximize the benefits of each graph owner, which is a common form of distributed graph learning under privacy-preserving conditions. As the landscape of local clients becomes increasingly diverse in terms of both model architectures and topological complexities, graph heterogeneity turns out to be one of the significant challenges to efficient collaboration among clients. Beyond existing paradigms, we delve a fresh insight into revisiting FGL as a semantic condensed graph alignment problem in this work. From this perspective, HashFGL is proposed for heterogeneous FGL through aligning condensed graphs via hashing in a symbiotic space. Specifically, the core of HashFGL lies in that it introduces a cross-client symbiotic space to facilitate effective collaboration. Within this space, an efficient hash-based semantic encoding strategy is proposed to model each local client while balancing coordinated resilience and semantic consistency. Furthermore, we derive an elaborate graph condenser based on the above strategy, which condenses original graphs with semantics and structure-preserving property, to maintain the effectiveness of condensed graph alignment for FGL. Formal theoretical analysis further reveals that HashFGL can effectively alleviate the problem of graph heterogeneity. Experimental results on three large-scale graphs, employing standard partitioning strategies and a pioneering, more realistic partitioning that we introduced, demonstrate the efficacy and scalability of HashFGL.