Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
Multimodal graph learning (MGL) integrates diverse modalities and structured context, but real-world graphs are often isolated and incomplete. Existing centralized MGL methods ignore knowledge sharing in federated settings, while federated MGL methods focus on non-graph data. This paper proposes FedMPO, addressing challenges of topology-isolated completion and reliability-imbalanced aggregation through topology-aware cross-modal generation, missing-aware expert routing, and reliability-aware aggregation. Experiments on 3 tasks across 6 datasets show performance gains up to 4.10% and 5.65% in high-missing and non-IID settings.
[2605.12584] Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
[Submitted on 12 May 2026]
Title:Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity
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Abstract:Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to support various network applications. However, real-world graphs are often isolated due to data-sharing limitations across multiple parties, and their modalities are frequently incomplete. This highlights an urgent need to develop a robust federated approach. However, we find that existing methods remain insufficient. On the one hand, centralized MGL methods that handle missing modalities overlook the knowledge sharing and generalization in federated scenarios. On the other hand, while federated MGL methods have become increasingly mature, they primarily target non-graph data. Based on these technologies, we identify a two-stage pipeline wherein client-side completion reconstructs missing modalities, and server-side aggregation integrates the client-updated parameters of both the modality generator and the backbone models. Although this serves as a general solution, we identify two primary challenges in achieving greater robustness: (1) Topology-Isolated Local Completion: Client-side modality generation struggles to effectively leverage global semantics. (2) Reliability-Imbalanced Global Aggregation: Server-side multi-party collaboration is hindered by client updates with varying modality availability and recovery reliability. To address these challenges, we propose \textsc{FedMPO}, which utilizes topology-aware cross-modal generation to recover missing features using comprehensive graph context, missing-aware expert routing to locally filter out noisy recovered signals, and reliability-aware aggregation to appropriately down-weight unreliable updates. Extensive experiments on 3 tasks across 6 datasets demonstrate that FedMPO outperforms baselines, achieving performance gains of up to 4.10% and 5.65% in high-missing and non-IID settings.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.12584 [cs.LG]
(or arXiv:2605.12584v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.12584
arXiv-issued DOI via DataCite (pending registration)
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From: Xunkai Li [view email] [v1] Tue, 12 May 2026 17:30:15 UTC (1,998 KB)
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