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.
Yeyu Yan, Shuai Zheng, Zhenfeng Zhu et al.· IEEE Transactions on Pattern...· 0 citations
Federated Graph Foundation Models (FedGFM) offer a decentralized GNN training paradigm that combines the collaborative training of federated graph learning (FGL) with the cross-domain generalization of graph foundation model (GFM). However, existing FedGFM methods still suffer from two key limitations, where (i) single-layer codebook quantization induces a representation bottleneck because the same token candidates are assigned to different nodes, reducing node discriminability and causing semantic confusion, and (ii) domain heterogeneity leads to cross-client semantic inconsistency, which weakens the reliability of server-side collaboration and aggregation. Based on these insights, we propose FedRAHi, a Reliability-Aware Hierarchical collaboration framework for FedGFM. Specifically, on the client side, we propose RQ-GAE, a Residual Quantized Graph Autoencoder. It quantizes nodes into tokens in multiple hierarchical codebooks, enhancing node discriminability and alleviating semantic confusion. These hierarchical codebooks can be further uploaded to the server as symbiotic knowledge to harmonize inter-client collaboration, since the codebooks do not directly expose graph data on each client. On the server side, to build a collaboration bridge across clients, we propose a Client Reliability Estimator (CRE). It leverages the symbiotic knowledge to construct a client-aware affinity graph, and performs personalized weighting of client parameters based on the reliability scores. Extensive experiments on seven public graph datasets across four domains and three downstream tasks show that FedRAHi consistently outperforms twelve SOTA baselines from both FGL and FedGFM.
Xiangkai Zhu, Yeyu Yan, Pengpeng Qiao et al.· Proceedings of the 32nd ACM...· 0 citations