OpenNovelty.org

Federated Graph-Level Clustering Network with Dual Knowledge Separation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
ClusteringDeep Graph LearningUnsupervised Learning
Abstract:

Federated Graph-level Clustering (FGC) offers a promising framework for analyzing distributed graph data while ensuring privacy protection. However, existing methods fail to simultaneously consider knowledge heterogeneity across intra- and inter-client, and still attempt to share as much knowledge as possible, resulting in consensus failure in the server. To solve these issues, we propose a novel Federated Graph-level Clustering Network with Dual Knowledge Separation (FGCN-DKS). The core idea is to decouple differentiated subgraph patterns and optimize them separately on the client, and then leverage cluster-oriented patterns to guide personalized knowledge aggregation on the server. Specifically, on the client, we separate personalized variant subgraphs and cluster-oriented invariant subgraphs for each graph. Then the former are retained locally for further refinement of the clustering process, while pattern digests are extracted from the latter for uploading to the server. On the server, we calculate the relation of inter-cluster patterns to adaptively aggregate cluster-oriented prototypes and parameters. Finally, the server generates personalized guidance signals for each cluster of clients, which are then fed back to local clients to enhance overall clustering performance. Extensive experiments on multiple graph benchmark datasets have proven the superiority of the proposed FGCN-DKS over the SOTA methods.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a dual knowledge separation framework for federated graph-level clustering, decoupling personalized variant subgraphs from cluster-oriented invariant subgraphs. It resides in the 'Graph-Level Clustering with Knowledge Separation' leaf, which contains only three papers including this work. This is a notably sparse research direction within the broader taxonomy of fifty papers, suggesting the specific combination of graph-level clustering and explicit knowledge separation remains relatively underexplored compared to adjacent areas like node-level clustering or non-graph federated clustering.

The taxonomy reveals substantial activity in neighboring branches. 'Soft Clustering and Structural Feature Alignment' (three papers) and 'Node-Level Graph Clustering' (three papers) address related graph clustering challenges but differ in granularity or aggregation strategy. Parallel branches such as 'Federated Clustering for Non-Graph Data' (ten papers across three sub-leaves) and 'Heterogeneous Graph Neural Networks in Federated Settings' (four papers) tackle heterogeneity in non-graph or multi-type graph contexts. The paper's focus on graph-level tasks with dual knowledge separation distinguishes it from these adjacent directions, which either operate at different granularities or lack explicit personalized-versus-cluster knowledge partitioning.

Among thirty candidates examined, none clearly refute any of the three contributions. Contribution A (dual knowledge separation framework) examined ten candidates with zero refutable overlaps; Contribution B (cluster-level aggregation strategy) and Contribution C (two-stage K-means with invariant/variant representations) each examined ten candidates with identical outcomes. This suggests that within the limited search scope, the specific combination of dual knowledge separation, cluster-oriented aggregation, and two-stage clustering for federated graph-level tasks appears relatively novel. However, the analysis is constrained to top-K semantic matches and does not constitute an exhaustive literature review.

Given the sparse leaf occupancy and absence of refutable prior work among thirty examined candidates, the contributions appear to occupy a distinct niche. The limited search scope means potentially relevant work outside the top-K semantic neighborhood may exist. The taxonomy context indicates the paper addresses a less crowded intersection of federated learning, graph-level clustering, and knowledge separation, though definitive novelty claims require broader literature coverage beyond the current analysis.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: federated graph-level clustering under heterogeneous data distributions. The field addresses the challenge of grouping entire graphs (rather than nodes or edges) when data are distributed across clients with non-identical statistical properties and privacy constraints. The taxonomy reveals a rich landscape organized around several complementary themes. Federated Graph Clustering Methods focus directly on clustering graph-structured data in federated settings, often employing knowledge separation or prototype-based techniques to handle client heterogeneity. Parallel branches such as Federated Clustering for Non-Graph Data and Multi-View Clustering in Federated Learning explore clustering under distribution shifts in tabular or multi-modal contexts, while Heterogeneous Graph Neural Networks in Federated Settings and Federated Learning for Non-IID Graph Classification tackle graph representation learning when clients hold diverse graph types or label distributions. Additional branches address system-level concerns: Personalized Federated Learning with Clustering and Hierarchical and Multi-Level Federated Learning examine how to tailor models or organize communication hierarchies, Decentralized Federated Learning Architectures and Graph Partitioning and Distributed Training Infrastructure consider peer-to-peer protocols and scalable partitioning, and Backdoor Defense and Security in Federated Learning alongside Heterogeneity-Aware Aggregation and Optimization ensure robustness and efficient parameter updates. A particularly active line of work centers on disentangling shared versus client-specific knowledge in graph clustering. Dual Knowledge Separation[0] exemplifies this approach by explicitly separating global cluster structures from local graph characteristics, closely aligning with Graph-Level Clustering[37] and FedPKA[39], which similarly emphasize prototype or knowledge alignment strategies to mitigate heterogeneity. In contrast, methods like Privacy Preserving Clustering[1] and Heterogeneous Graph Privacy[3] prioritize differential privacy guarantees, trading off some clustering fidelity for stronger confidentiality. Meanwhile, works such as Soft Clustering Federated[2] and Heterogeneity Aware Clustering[10] explore probabilistic or adaptive aggregation schemes that accommodate varying client distributions without strict knowledge partitioning. The interplay between privacy, personalization, and clustering quality remains an open question, with Dual Knowledge Separation[0] positioned among efforts that leverage structural inductive biases to achieve both accurate global clustering and respect for local data diversity.

Claimed Contributions

Dual knowledge separation framework for federated graph-level clustering

The authors introduce FGCN-DKS, a framework that separates graph knowledge at two levels: within clients (personalized variant subgraphs vs. cluster-oriented invariant subgraphs) and across clients (cluster-oriented consensus patterns vs. client-driven knowledge). This dual separation enables privacy-preserving clustering while addressing both intra-client and inter-client heterogeneity.

10 retrieved papers
Common Knowledge Sharing Strategy (CKSS) for cluster-level aggregation

The authors propose CKSS, a server-side aggregation mechanism that computes cluster-level affinities using graph kernels on pattern digests and performs personalized aggregation based on these affinities. This strategy enables finer-grained consensus by identifying semantically consistent components across clients rather than naive parameter averaging.

10 retrieved papers
Two-stage K-means clustering with invariant and variant representations

The authors develop a two-stage clustering approach that first performs K-means on invariant representations to capture cluster-oriented stable patterns, then refines clusters using variant representations to adapt to client-specific information. This design leverages both shared and personalized knowledge for improved clustering quality.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Dual knowledge separation framework for federated graph-level clustering

The authors introduce FGCN-DKS, a framework that separates graph knowledge at two levels: within clients (personalized variant subgraphs vs. cluster-oriented invariant subgraphs) and across clients (cluster-oriented consensus patterns vs. client-driven knowledge). This dual separation enables privacy-preserving clustering while addressing both intra-client and inter-client heterogeneity.

Contribution

Common Knowledge Sharing Strategy (CKSS) for cluster-level aggregation

The authors propose CKSS, a server-side aggregation mechanism that computes cluster-level affinities using graph kernels on pattern digests and performs personalized aggregation based on these affinities. This strategy enables finer-grained consensus by identifying semantically consistent components across clients rather than naive parameter averaging.

Contribution

Two-stage K-means clustering with invariant and variant representations

The authors develop a two-stage clustering approach that first performs K-means on invariant representations to capture cluster-oriented stable patterns, then refines clusters using variant representations to adapt to client-specific information. This design leverages both shared and personalized knowledge for improved clustering quality.

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