Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 6958-6968· 0 citations· 11 references
Abstract
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.
FedTopo is proposed, a relation-level framework that encodes global knowledge as class relation topology, capturing how classes relate within each client rather than where they lie in feature space.
This work proposes a FedIGL framework based on invariant learning, which effectively disrupts spurious correlations and further mines the invariant factors across different distributions, and proposes a novel Bi-Gradient Regularization strategy that introduces gradient constraints to guide the model in identifying client-agnostic and client-specific subgraph patterns for better graph representations.
Lingren Wang, Wenxuan Tu, Jiaxin Wang et al.· Neural Information Processin...· 2 citations
FedDUA is proposed, a novel disagreement-aware and uncertainty-guided framework for subgraph FL that first models cross-client semantic disagreement via a lightweight semantic anchor graph and derives adaptive aggregation weights for reliable global federated knowledge.
Keao Xi, Nannan Wu, Yiming Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
Federated learning (FL) collaboratively trains models across networked industrial Internet of Things (IIoT) terminals. However, statistical heterogeneity in IIoT data often hinders the performance of global models. Current FL methods typically focus on single-level representation alignment and fail to exploit gradient-guided semantic feedback across hierarchical layers. To address statistical heterogeneity, we propose federated hierarchical prototype learning (FedHiPL), which models local representations with Gaussian prototypes and improves global learning through hierarchical prototype calibration. First, FedHiPL performs multilevel prototype alignment based on symmetric Kullback–Leibler divergence to enforce representation consistency across layers. Second, FedHiPL calibrates the local decision head by balancing local and global decision objectives with decision consistency constraints. Third, FedHiPL rectifies global prototypes through a gradient-guided hierarchical calibration module to maintain structural consistency across network layers. Experiments on a custom-constructed distributed cluster demonstrate that FedHiPL achieves 93.24% accuracy on Edge-IIoT and 72.36% accuracy on UNSW-NB15 under strong statistical heterogeneity, outperforming the representative prototype-based baseline FedProto by 6.06% and 15.80%, respectively.
Fangyu Li, Zhi-Cheng Chen, Jinghao Liu et al.· IEEE Internet of Things Jour...· 0 citations
Prototype-based knowledge sharing effectively mitigates data and model heterogeneity in federated learning (FL) by exchanging class-level semantic information. However, existing methods typically assume all local prototypes are equally reliable. Consequently, low-quality prototypes from heterogeneous models or dynamic clients can contaminate the global aggregation, leading to a vicious cycle of noise accumulation and performance degradation. To address this, we propose FedLEAF, a Federated Learning framework with server-side proactive Evaluation and clientside Adaptive Fusion. Specifically, the server employs an Adaptive Learning Prototypes (ALP) network to dynamically evaluate prototype reliability and generate learnable aggregation weights, ensuring that highquality prototypes exert a primary influence on the global model. Meanwhile, the client utilizes a Historical Consistency Fusion (HCF) strategy to selectively absorb global knowledge by assessing its consistency with locally maintained historical prototypes. Extensive experiments on standard datasets demonstrate that FedLEAF achieves effective improvements in model accuracy and robustness compared to existing methods.
Zhiyuan Zhu, Si-Yi Deng, Dapeng Wu et al.· 2026 International Conferenc...· 0 citations
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