# Federated Graph Learning with Structure Proxy Alignment

arXiv: https://arxiv.org/abs/2408.09393 · HTML: https://mlsanity.com/paper/2408.09393/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation](https://mlsanity.com/paper/2110.08727/) (2021)
- [Subgraph Federated Learning with Missing Neighbor Generation](https://mlsanity.com/paper/2106.13430/) (2021)
- [Federated Graph Classification over Non-IID Graphs](https://mlsanity.com/paper/2106.13423/) (2021)
- [Community detection in networks: A user guide](https://mlsanity.com/paper/1608.00163/) (2016)
- [Federated Learning on Non-IID Graphs via Structural Knowledge Sharing](https://mlsanity.com/paper/2211.13009/) (2022)
- [Inductive Entity Representations from Text via Link Prediction](https://mlsanity.com/paper/2010.03496/) (2020)
- [FedE: Embedding Knowledge Graphs in Federated Setting](https://mlsanity.com/paper/2010.12882/) (2020)
- [TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification](https://mlsanity.com/paper/2206.12917/) (2022)
- [Federated Graph Semantic and Structural Learning](https://mlsanity.com/paper/2406.18937/) (2024)
- [FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction](https://mlsanity.com/paper/2112.10166/) (2021)
- [Synthetic Graph Generation to Benchmark Graph Learning](https://mlsanity.com/paper/2204.01376/) (2022)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Spatial-Temporal Networks for Antibiogram Pattern Prediction](https://mlsanity.com/paper/2305.01761/) (2023)
- [Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training](https://mlsanity.com/paper/2404.13349/) (2024)
- [Federated Few-shot Learning](https://mlsanity.com/paper/2306.10234/) (2023)

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [Curriculum Guided Personalized Subgraph Federated Learning](https://mlsanity.com/paper/2509.00402/) (2025)
- [Federated Graph Learning with Graphless Clients](https://mlsanity.com/paper/2411.08374/) (2024)
- [Personalized One-shot Federated Graph Learning for Heterogeneous Clients](https://mlsanity.com/paper/2411.11304/) (2024)
- [Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning](https://mlsanity.com/paper/2412.19229/) (2024)
- [Out-of-Distribution Generalization on Graphs via Progressive Inference](https://mlsanity.com/paper/2503.02988/) (2025)
- [Towards Unbiased Federated Graph Learning: Label and Topology Perspectives](https://mlsanity.com/paper/2504.09963/) (2025)
- [FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs](https://mlsanity.com/paper/2504.21206/) (2025)
- [GraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generation](https://mlsanity.com/paper/2508.10471/) (2025)
- [Dissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis](https://mlsanity.com/paper/2509.18171/) (2025)
- [Robust Client-Server Watermarking for Split Federated Learning](https://mlsanity.com/paper/2511.13598/) (2025)
- [BoostFGL: Boosting Fairness in Federated Graph Learning](https://mlsanity.com/paper/2601.16496/) (2026)
- [Low-pass Personalized Subgraph Federated Recommendation](https://mlsanity.com/paper/2603.20338/) (2026)
- [Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach](https://mlsanity.com/paper/2606.24237/) (2026)

## Abstract (arXiv)

Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as social recommendation and financial fraud detection. Inherited from generic Federated Learning (FL), FGL similarly has the data heterogeneity issue where the label distribution may vary significantly for distributed graph data across clients. For instance, a client can have the majority of nodes from a class, while another client may have only a few nodes from the same class. This issue results in divergent local objectives and impairs FGL convergence for node-level tasks, especially for node classification. Moreover, FGL also encounters a unique challenge for the node classification task: the nodes from a minority class in a client are more likely to have biased neighboring information, which prevents FGL from learning expressive node embeddings with Graph Neural Networks (GNNs). To grapple with the challenge, we propose FedSpray, a novel FGL framework that learns local class-wise structure proxies in the latent space and aligns them to obtain global structure proxies in the server. Our goal is to obtain the aligned structure proxies that can serve as reliable, unbiased neighboring information for node classification. To achieve this, FedSpray trains a global feature-structure encoder and generates unbiased soft targets with structure proxies to regularize local training of GNN models in a personalized way. We conduct extensive experiments over four datasets, and experiment results validate the superiority of FedSpray compared with other baselines. Our code is available at https://github.com/xbfu/FedSpray.