Title: MOTION: Multi-Sculpt Evolutionary Coarsening for Federated Continual Graph Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Federated Learning

Graph Learning

Continual Learning
Abstract: Graph neural networks (GNNs) have achieved remarkable success in various domains but typically rely on centralized, static graphs, which limits their applicability in distributed, evolving environments. To address this limitation, we define the task of Federated Continual Graph Learning (FCGL), a paradigm for incremental learning on dynamic graphs distributed across decentralized clients. Existing methods, however, neither preserve graph topology during task transitions nor mitigate parameter conflicts in server‐side aggregation. To overcome these challenges, we introduce **MOTION**, a generalizable FCGL framework that integrates two complementary modules: the Graph Topology‐preserving Multi‐Sculpt Coarsening (G‐TMSC) module, which maintains the structural integrity of past graphs through a multi‐expert, similarity‐guided fusion process, and the Graph‐Aware Evolving Parameter Adaptive Engine (G‐EPAE) module, which refines global model updates by leveraging a topology‐sensitive compatibility matrix. Extensive experiments on real‐world datasets show that our approach improves average accuracy (AA) by an average of 30\% $\uparrow$ over the FedAvg baseline across five datasets while maintaining a negative $\downarrow$ average forgetting (AF) rate, significantly enhancing generalization and robustness under FCGL settings. The code is available for anonymous access at https://anonymous.4open.science/r/MOTION.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MOTION method for federated continual graph learning that addresses the challenges of learning on dynamic, distributed graph data. The method maintains structural integrity through a multi-expert fusion process and optimizes global model updates using a topology-sensitive compatibility matrix. Experiments on benchmark datasets demonstrate the performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The graph federated continual learning task is meaningful, with its challenges clearly defined.\n2. Experiments are conducted across five datasets, and ablation studies are presented.\n\nWeaknesses:\n1. The method's motivation and description are unclear. The framework diagram is too abstract to correspond with the text.\n2. Some baselines are missing in the experiments, and the baseline references are inconsistent.\n\nQUESTIONS:\n1. Figure 2 needs to be redrawn to better illustrate the method. While visually appealing, it lacks specificity. For instance, Multi-Sculpt isn't shown, and some modules are represented by symbolic images, making it hard to link components to the method description (e.g., Expert Scores). A more detailed figure would enhance the reader's understanding.\n2. The specific method process is unclear and needs a more comprehensive explanation from the authors. In Section 3.2, please clarify:\n    - The construction of the node mapping table. How node features from other tasks are aggregated without saving the entire graph?\n    - How does the multi-sculpt coarsening framework systematically prune excess nodes and suppress noise?\n    - The process of the reservoir sampling-based dynamic memory mechanism.\n3. On line 215 of page 6, how can Parameters $\\Delta \\theta_k$ capture topological changes? The model parameters don't seem to have a direct link to the topological structure.\n4. In Table 1, too few comparison methods are used in the experiments. More graph federated learning and graph continual learning baselines need to be included.\n5. In Section 4.1, the experimental baselines are inconsistently referenced. The baseline names don't match the cited papers.\n6. It is better to computer the average of multiple runs to demonstrate the method's stability.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenges inherent in FCGL, specifically the preservation of graph topology and mitigation of parameter conflicts during model aggregation. MOTION integrates a G-TMSC module to maintain structural integrity via multi-expert node merging, and a G-EPAE to refine aggregation by dynamically adjusting based on graph compatibility. Extensive experiments on real-world datasets demonstrate that MOTION achieves significant improvements in accuracy and resilience against forgetting compared to existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. MOTION combines graph coarsening with expert fusion and adaptive aggregation based on a compatibility matrix. These techniques are tailored specifically for FCGL, where existing solutions are lacking, demonstrating a thoughtful methodological advancement.\n2. This paper is clearly and effectively written, with a fancy and comprehensive visual representation of the framework. The accurate and thorough explanations of the methodological components further contribute to its ease of understanding.\n3. The experiments are comprehensive, covering five real-world datasets with evaluations on both Average Accuracy and Average Forgetting. The empirical results show substantial gains over strong baselines, supporting the claimed improvements.\n\nWeaknesses:\n1. The work does not provide sufficient information on the experimental implementation used for other conventional FL、FGL、CGL and FCL baselines. \n2. Although the method components (G-TMSC and G-EPAE) are well-explained, the authors should provide a clearer rationale for why these specific designs are necessary to address the stated problems, introducing their connections to the initial motivations in the FCGL setting.\n3. The paper does not investigate how the size of the original subgraphs at each time step influences the overall performance. A comprehensive sensitivity study on subgraph scale would help clarify whether the proposed method remains effective across varying graph sizes.\n\nQUESTIONS:\n1. Complementation of the explanation negative forgetting phenomenon. While the experimental results report negative AF, the authors should provide a more detailed analysis of this phenomenon. A clearer explanation connecting this observation to the roles of G-TMSC and G-EPAE would strengthen the empirical claims and provide insight into the learning dynamics under the FCGL setting.\n2. Complementation of the explanation for the multi-expert architecture input. The authors should clarify why they choose to decompose the merged graph into components such as 1-hop and 2-hop subgraphs, cosine similarity, and other metrics as inputs to the multi-expert architecture.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a comprehensive evaluation of the proposed MOTION framework on five benchmark datasets in the FCGL setting. The results demonstrate substantial improvements over state-of-the-art methods in terms of both average accuracy and average forgetting, often showing negative forgetting rates. The ablation studies highlight the importance of both the graph coarsening and parameter alignment modules. Additional experiments on hyperparameter sensitivity and scalability are included to support robustness.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. It is novel and well-motivated to propose an FCGL approach. The paper clearly identifies the key challenges faced by existing FCGL methods and successfully provides solutions to overcome them.\n2. Both the problem description and the framework illustration are clearly and thoroughly presented.\n3. The integration of G-TMSC and G-EPAE demonstrates a practical understanding of the challenges. \n\n**Weaknesses:**\n1. The paper lacks long-range temporal retention analysis. It does not analyze how the model preserves information from earlier tasks, given that aggregation is limited to the most recent timestamp.\n2. For a federated learning setting, the paper is surprisingly silent on how MOTION handles or could be extended to address privacy or adversarial issues, which are critical in real deployments.\n3. The paper lacks a sensitivity study. It does not analyze how the performance of the global model varies with different numbers of local training epochs.\n\nQUESTIONS:\n1. How are the directional and magnitude components of the parameter increment computed to form the compatibility matrix and determine parameter importance?\n2. Why does the proposed problem formulation require a distinct FCGL setting, rather than adapting existing FCL or FGL frameworks?\n3. How does the proposed MOTION framework interact with the underlying GNN architecture, and is it compatible with other models?\n4. How does G-TMSC scale when the number of nodes is in the order of millions? How costly is it to compute all the structural metrics (especially PageRank, centrality scores) on-device?\n5. The coarsening process includes a reservoir sampling memory. What is the size of this buffer, and how sensitive is MOTION to the buffer budget?\n\nLIMITATIONS:\nThe authors should further discuss the societal impact of the proposed method.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces the federated graph continual learning framework and presents an effective instantiation consisting of two elements: one integrates multiple structural metrics, while the other ensures appropriate aggregation from different nodes. Experiments demonstrate consistent performance improvements over the baselines.\n\nSTRENGTHS AND WEAKNESSES:\nTo my knowledge, the FGCL framework is novel, and the proposed MOTION method shows better figures over the baselines, which is another notable strength of the paper.\n\nHowever, in its current form, the paper falls short of publication standards in terms of presentation quality, clarity, and rigor. \nI believe the paper requires substantial revision to ensure that readers can grasp the context and fully appreciate its value. It also makes it difficult for me to provide an accurate assessment of the paper's technical content. Several examples of my concerns are outlined below.\n\nQUESTIONS:\nPaper presentation requires polishing:\n- The question in line 42 mentions FCL for the first time, which does not seem to fit into the preceding framing of the discussion where GNN and FGL are introduced and FGCL is described as FGL+CGL.\n- Lines 43-50. The concept of preserving inherent graph topology and topological integrity is central to the paper, yet it is mentioned without definition. I suggest that the authors provide a clear introduction to these concepts and explain why they are desirable properties.\n- Line 128. I was unable to discern the substantial topological heterogeneity and neighborhood distributions from Figure 1. Please revise the figure and its caption to clarify these points.\n- I find Figures 1 and 2 difficult to interpret. They are overly dense and include numerous distracting elements whose purpose is unclear and insufficiently explained. For example, in Figure 1: Q&A, teacher, the cloud, question marks, a head with charts, \"FAIL,\" the evil figure, the magnifying lens, and the arrows connecting the left and right blocks; the same applies to Figure 2.\n- Many of the symbols used in introducing the similarities discussed in \"Multi-Sculpt Coarsening\" are undefined or were already introduced in earlier sections. Additionally, what exactly are the inputs to each of these symbols?\n- The acronyms and references for the baselines need revision, e.g., NeurIPS 2025, AISTATS, and avoiding arXiv versions when the paper has been published elsewhere.\n\nSome claims are not sufficiently supported by evidence, which makes them unconvincing:\n- Lines 63-65. It would be helpful if the authors could substantiate the claimed general failure with evidence and/or references. It would also improve clarity if the authors elaborated at this point on the specific challenges of the new setting, which ultimately lead to the research question posed at the end of the paragraph.\n- Equation 1. The loss function presented in Equation 1 and the accompanying discussion in lines 104-106 are not convincing. There appears to be no clear reason to add the term $\\mathcal R(\\theta)$ to mitigate forgetting, since $\\mathcal P_\\text{total}(\\theta)$ already accounts for all tasks.\n- Lines 121-125. Could the authors provide evidence and discussion showing that these methods suffer from the three identified shortcomings, i.e., failing to deliver comparable performance on graph-structured data, requiring excessive storage, and experiencing conflicts?\n- Did I understand correctly that the \"generalizable FGCL design principles\" are the same as those from federated and continual learning, except for storage efficiency? If so, why would storage efficiency differ from that in CGL?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 2, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}