# FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs

arXiv: https://arxiv.org/abs/2208.13685 · HTML: https://mlsanity.com/paper/2208.13685/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [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)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking](https://mlsanity.com/paper/1707.03815/) (2017)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2007.02901/) (2020)
- [A Note on Over-Smoothing for Graph Neural Networks](https://mlsanity.com/paper/2006.13318/) (2020)
- [Federated Learning of a Mixture of Global and Local Models](https://mlsanity.com/paper/2002.05516/) (2020)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [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)
- [FedMix: Approximation of Mixup under Mean Augmented Federated Learning](https://mlsanity.com/paper/2107.00233/) (2021)
- [Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization](https://mlsanity.com/paper/2009.05204/) (2020)
- [GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://mlsanity.com/paper/2012.04187/) (2020)
- [FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning](https://mlsanity.com/paper/2204.05562/) (2022)
- [FedGL: Federated Graph Learning Framework with Global Self-Supervision](https://mlsanity.com/paper/2105.03170/) (2021)
- [A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy](https://mlsanity.com/paper/2206.03492/) (2022)

## Built on by (most cited first, 12 of 12)

- [Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement](https://mlsanity.com/paper/2505.12684/) (2025)
- [FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling](https://mlsanity.com/paper/2510.07755/) (2025)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning](https://mlsanity.com/paper/2502.16793/) (2025)
- [Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections](https://mlsanity.com/paper/2505.23864/) (2025)
- [Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity](https://mlsanity.com/paper/2608.00623/) (2026)
- [Federated Graph Learning with Adaptive Importance-based Sampling](https://mlsanity.com/paper/2409.14655/) (2024)
- [Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks](https://mlsanity.com/paper/2506.12425/) (2025)
- [Sharpness-aware Federated Graph Learning](https://mlsanity.com/paper/2512.16247/) (2025)
- [DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2605.31427/) (2026)
- [PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning](https://mlsanity.com/paper/2609.04832/) (2026)

## Abstract (arXiv)

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some practical scenarios, graph data are stored separately in multiple distributed parties, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo problems while preserving the privacy of each party (or client). Nevertheless, different graph data distributions among various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this paper, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, where each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.