Title: HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Federated Learning

Graph Learning

Robustness

Geometric Learning
Abstract: Robust Federated Graph Learning (FGL) provides an effective decentralized framework for training Graph Neural Networks (GNNs) in noisy-label environments. However, the subtlety of noise during training presents formidable obstacles for developing robust FGL systems. Previous robust FL approaches neither adequately constrain edge-mediated error propagation nor account for intra-class topological differences. At the client level, we innovatively demonstrate that hyperspherical embedding can effectively capture graph structures in a fine-grained manner. Correspondingly, our method effectively addresses the aforementioned issues through fine-grained hypersphere alignment. Moreover, we uncover undetected noise arising from localized perspective constraints and propose the geometric-aware hyperspherical purification module at the server level. Combining both level strategies, we present our robust FGL framework,**HYPERION**, which operates all components within a unified hyperspherical space. **HYPERION** demonstrates remarkable robustness across multiple datasets, for instance, achieving a 29.7\% $\uparrow$ F1-macro score with 50\%-pair noise on Cora. The code is available for anonymous access at \url{https://anonymous.4open.science/r/Hyperion-NeurIPS/}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study tackles challenges such as non-IID graph data distribution and adversarial noise perturbation in federated graph learning. It introduces a robust strategy where clients collaboratively learn hyperspherical embeddings to enforce geometric consistency across heterogeneous local graphs, mitigating feature skew. To address the classic differences in client data distribution in federated learning, the approach incorporates Geometric-Aware Hyperspherical Purification. Lastly, adequate experiments are conducted to verify the model effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. It is novel and meaningful to propose a robust FGL approach. This study precisely identifies the noise challenges of FGL and tackles them effectively. The overall approach is well-motivated, and the motivation is also explained with clarity.\n\n2. The figures of problem and framework illustration are clear and detailed, and the equations and explanations are reasonable. I believe this is a comprehensive framework for the graph data.\n\n3. The approach proposed in the study is consistent and natural. The key modules and their components are innovative and logically coherent. I believe this approach is inspiring.\n\nWeaknesses:\n\n1. The work lacks analysis of model performance with different local training epochs. Can the author supplement the training effects of his method under different training rounds?\n\n2. It would be better if the reasons for the selection of comparison algorithms and datasets were explained.\n\nQUESTIONS:\n1. The author did not introduce how to create enhanced views in HS-CNC.\n2. Why did the author project the nodes onto the hypersphere? Can the author explain the benefits of doing so?\n\nLIMITATIONS:\nMy complete set of queries and concerns is captured in the 'Weaknesses' and 'Questions' part.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nHYPERION proposes a federated graph learning framework that handles noisy labels and complex topologies using hyperspherical embeddings. Its multi-prototype design captures fine-grained structures, while noise calibration and purification (via Wasserstein distillation and Mahalanobis-based methods) enhance robustness. Experiments show strong performance across datasets and noise levels, outperforming existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper points out several main reasons why noise problems in FGL are difficult to solve. Subtle differences in graph structures and the classic differences in client data distribution in federated learning indeed make it difficult to effectively identify noise. The problem illustration clearly shows this phenomenon. The author's writing and illustrations made it easy for me to understand his intentions.\n\n2. The proposed method of robust FGL appears interesting and innovative. The paper provides a detailed explanation of the methodology. The paper considers the difference between normal topological variations and subtle noise, introducing Topological Prototypes Hyperspherical Learning to fully capture the rich topological differences between nodes of the same class. I find this approach inspiring.\n\n3. This paper conducted comprehensive experiments, compared multiple methods, and provided a persuasive explanation of the experimental results. The proposed method makes sense for the problem. The node classification task is widely used as a downstream task in FGL methods and is practically applicable. Overall, the approach appears to be both methodologically sound and relevant to real-world scenarios.\n\n\nWeaknesses:\n\n1. On page 9, the author discusses the impact of different numbers of prototypes in prototype clusters on performance under different noise conditions. However, the author does not introduce the experimental results under the F1-macro metric, nor does he provide information on other hyperparameters besides K for this experiment.\n\n2. The TP-HSL component of your approach introduces multiple prototypes per class. Why does this method enable fine-grained structural learning?\n\nQUESTIONS:\nSee Strengths And Weaknesses.\n\nLIMITATIONS:\nIn the experiment, the authors compared several robust graph learning methods, but they did not explain how to apply these methods in a federated learning scenario.I hope the authors will introduce how they applied these methods to the federal framework.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces HYPERION, a robust federated graph learning framework designed to handle complex topologies and resist malicious noise by leveraging hyperspherical embeddings.\nClients embed local node samples onto a hypersphere with class prototypes and generate perturbed views to identify noise-sensitive regions. During global training, a Gaussian Mixture Model filters clean clients. Prototypes from these trusted clients are aggregated and distilled into a global hypersphere, while biased nodes are pruned to ensure model integrity. Experiments confirm that HYPERION outperforms existing methods and shows remarkable resilience to severe label noise.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:  \nS1: Novel and well-motivated. The authors identify the limitations of coarse-grained representations and the neglect of intra-class topological differences in existing robust FGL methods. It innovatively proposes using hyperspherical embeddings to capture fine-grained structural patterns, providing an approach to mitigate noise propagation.  \nS2: Effective and thought-provoking. The introduction of multiple topological prototypes per class allows the model to capture subtle structural variations within the same class, a crucial aspect of graph data that is often overlooked. This design significantly heightens the ability to differentiate noisy signals from valid ones. Good job!  \nS3: Comprehensive framework. The interplay between local noise calibration and global geometric-aware purification creates a holistic defense mechanism against label noise . Furthermore, there has been little work done in this area previously, and the author has indeed solved the problem.  \nS4: Strong empirical performance. The experimental validation is extensive and compelling. HYPERION consistently and significantly outperforms a wide array of state-of-the-art methods across multiple datasets and under various noise conditions.  \n\nWeaknesses:  \nW1: The authors use Mahalanobis distance when pruning and Wasserstein distance when distilling after clustering, but did not explain in detail the characteristics of these two distances and the differences between them.  \nW2: The authors compare the proposed approach with several traditional FL/FGL and robust FL and GL methods, but additional comparisons with more FGL and robust FL and GL methods could strengthen this study.\n\nQUESTIONS:\nInsufficient methodological justification. The authors employ Mahalanobis distance distillation in GA-HSP but do not provide a thorough rationale for this choice. A more detailed discussion comparing it with alternative distance metrics (e.g., standard L2 distance) would strengthen the methodological foundation.\n\nLIMITATIONS:\nI have put all my doubts in Weaknesses and Questions.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work presents HYPERION which is a collection of 3 complementary techniques to deal with graph learning in the presence of noisy/incorrect node labels. The basic foundation of their approach relies on projecting the node features into a hypersphere representation  where nodes with similar features end up on the same region on the hypersphere. Based on that assumption the second step randomly drops edges in the graph and re-assesses the node's hypersphere neighborhood. For nodes that are stable the neighborhood stays the same in presence of small perturbations in the graph. When such stable nodes are identified they are used as the clean nodes whose labels can be more trusted. Finally, the third technique uses the global knowledge of information gained from each client in a federated setting to provide a mechanism to identify noisy nodes and propagate that knowledge to each client so that the global model stays robust. These 3 techniques are combined into a single framework called HYPERION which is then evaluated extensively over multiple datasets to quantify the performance.\n\nSTRENGTHS AND WEAKNESSES:\n+ The work presents a novel approach to projecting the node features into a hyperdimensional space to identify node clusters. \n+ The 3 components are tied nicely together to generate a unified framework. \n+ Results are sufficiently detailed. \n\n- The main criticism is that the assumption that node features projected into hyperdimensional space can automatically form clusters that can be used to identify node label noise. It is unclear whether graph nodes with nearly identical features can potentially have different labels. For instance, consider two nodes that share many common features except their gender value. Their labels may infact differ vastly given that some features contribute  more to the overall label than other features. But that is treated as noise in the presented setting which may not be a good assumption. \n- On the same vein, identifying stable nodes by randomly deleting edges or other nodes in the graph is also an empirical observation without much sound theoretical basis. In fact a few key edges may change the learning behavior. \n\nAddressing the above two assumptions is important to make the paper more interesting to the audience.\n\nQUESTIONS:\nI have two questions I posed regarding the assumptions and theoretical foundation for the assumptions made in the work. I would appreciate some insight or additional commentary on the two above mentioned weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-26"}