# Federated Graph Semantic and Structural Learning

arXiv: https://arxiv.org/abs/2406.18937 · HTML: https://mlsanity.com/paper/2406.18937/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 69 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Relational Knowledge Distillation](https://mlsanity.com/paper/1904.05068/) (2019)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Contrastive Multi-View Representation Learning on Graphs](https://mlsanity.com/paper/2006.05582/) (2020)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks](https://mlsanity.com/paper/2004.05937/) (2020)
- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Unsupervised Embedding Learning via Invariant and Spreading Instance Feature](https://mlsanity.com/paper/1904.03436/) (2019)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [SEED: Self-supervised Distillation For Visual Representation](https://mlsanity.com/paper/2101.04731/) (2021)
- [Subgraph Federated Learning with Missing Neighbor Generation](https://mlsanity.com/paper/2106.13430/) (2021)

## Built on by (most cited first, 30 of 30)

- [LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering](https://mlsanity.com/paper/2412.12359/) (2024)
- [OpenFGL: A Comprehensive Benchmark for Federated Graph Learning](https://mlsanity.com/paper/2408.16288/) (2024)
- [Federated Graph Learning with Structure Proxy Alignment](https://mlsanity.com/paper/2408.09393/) (2024)
- [Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement](https://mlsanity.com/paper/2505.12684/) (2025)
- [Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph](https://mlsanity.com/paper/2410.00049/) (2024)
- [GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning](https://mlsanity.com/paper/2410.13761/) (2024)
- [FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling](https://mlsanity.com/paper/2510.07755/) (2025)
- [S2FGL: Spatial Spectral Federated Graph Learning](https://mlsanity.com/paper/2507.02409/) (2025)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection](https://mlsanity.com/paper/2605.09428/) (2026)
- [Federated Continual Graph Learning](https://mlsanity.com/paper/2411.18919/) (2024)
- [Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective](https://mlsanity.com/paper/2502.13732/) (2025)
- [Federated Graph Learning with Graphless Clients](https://mlsanity.com/paper/2411.08374/) (2024)
- [Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning](https://mlsanity.com/paper/2412.19229/) (2024)
- [Data-centric Federated Graph Learning with Large Language Models](https://mlsanity.com/paper/2503.19455/) (2025)
- [Rethinking Client-oriented Federated Graph Learning](https://mlsanity.com/paper/2504.14188/) (2025)
- [Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach](https://mlsanity.com/paper/2601.21369/) (2026)
- [DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism](https://mlsanity.com/paper/2409.00614/) (2024)
- [Federated Prototype Graph Learning](https://mlsanity.com/paper/2504.09493/) (2025)
- [Towards Unbiased Federated Graph Learning: Label and Topology Perspectives](https://mlsanity.com/paper/2504.09963/) (2025)
- [GraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generation](https://mlsanity.com/paper/2508.10471/) (2025)
- [Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework](https://mlsanity.com/paper/2607.15687/) (2026)
- [Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity](https://mlsanity.com/paper/2608.00623/) (2026)
- [Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics](https://mlsanity.com/paper/2412.18845/) (2024)
- [Knowledge-Driven Federated Graph Learning on Model Heterogeneity](https://mlsanity.com/paper/2501.12624/) (2025)
- [Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis](https://mlsanity.com/paper/2509.18171/) (2025)
- [Sharpness-aware Federated Graph Learning](https://mlsanity.com/paper/2512.16247/) (2025)
- [Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration](https://mlsanity.com/paper/2605.06260/) (2026)
- [Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning](https://mlsanity.com/paper/2609.02967/) (2026)
- [Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach](https://mlsanity.com/paper/2609.26063/) (2026)

## Abstract (arXiv)

Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenges. Most relative arts focus on traditional distributed tasks like images and voices, incapable of graph structures. This paper firstly reveals that local client distortion is brought by both node-level semantics and graph-level structure. First, for node-level semantics, we find that contrasting nodes from distinct classes is beneficial to provide a well-performing discrimination. We pull the local node towards the global node of the same class and push it away from the global node of different classes. Second, we postulate that a well-structural graph neural network possesses similarity for neighbors due to the inherent adjacency relationships. However, aligning each node with adjacent nodes hinders discrimination due to the potential class inconsistency. We transform the adjacency relationships into the similarity distribution and leverage the global model to distill the relation knowledge into the local model, which preserves the structural information and discriminability of the local model. Empirical results on three graph datasets manifest the superiority of the proposed method over its counterparts.