FedRAHi, a Reliability-Aware Hierarchical collaboration framework for FedGFM that leverages the symbiotic knowledge to construct a client-aware affinity graph, and performs personalized weighting of client parameters based on the reliability scores is proposed.
Xiangkai Zhu, Yeyu Yan, Peng-Peng Qiao et al.· Proceedings of the 32nd ACM...· 0 citations
An efficient hash-based semantic encoding strategy is proposed to model each local client while balancing coordinated resilience and semantic consistency and an elaborate graph condenser is derived based on the above strategy, which condenses original graphs with semantics and structure-preserving property to maintain...
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...
Xiangkai Zhu, Yeyu Yan, Pengpeng Qiao et al.· Proceedings of the 32nd ACM...· 0 citations
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