arXiv Machine Learning July 31, 2026 By Zhanting Zhou, Kahou Tam, Zeyu Ma, Ziqiang Zheng

Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis

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arXiv:2509. 18171v5 Announce Type: replace Abstract: Federated graph learning (FGL) trains a shared graph model across clients whose local graphs differ in node features, labels, and connectivity while keeping raw graph data decentralized.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv:2509. 18171v4 Announce Type: replace Abstract: Federated graph learning (FGL) is a natural paradigm for social-media user graphs, where language communities, regional markets, and service boundaries can prevent raw graph pooling.

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FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning

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