# AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

arXiv: https://arxiv.org/abs/2401.11750 · HTML: https://mlsanity.com/paper/2401.11750/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 77 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 30 of 55)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [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)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Pitfalls of Graph Neural Network Evaluation](https://mlsanity.com/paper/1811.05868/) (2018)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Geom-GCN: Geometric Graph Convolutional Networks](https://mlsanity.com/paper/2002.05287/) (2020)
- [Adaptive Universal Generalized PageRank Graph Neural Network](https://mlsanity.com/paper/2006.07988/) (2020)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [A critical look at the evaluation of GNNs under heterophily: Are we really making progress?](https://mlsanity.com/paper/2302.11640/) (2023)
- [Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods](https://mlsanity.com/paper/2110.14446/) (2021)
- [Adversarial Attacks on Graph Neural Networks via Meta Learning](https://mlsanity.com/paper/1902.08412/) (2019)
- [Graph Neural Networks with Heterophily](https://mlsanity.com/paper/2009.13566/) (2020)
- [Combining Label Propagation and Simple Models Out-performs Graph Neural Networks](https://mlsanity.com/paper/2010.13993/) (2020)
- [Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks](https://mlsanity.com/paper/2102.06462/) (2021)
- [Is Homophily a Necessity for Graph Neural Networks?](https://mlsanity.com/paper/2106.06134/) (2021)
- [Finding Global Homophily in Graph Neural Networks When Meeting Heterophily](https://mlsanity.com/paper/2205.07308/) (2022)
- [Graph Neural Networks for Graphs with Heterophily: A Survey](https://mlsanity.com/paper/2202.07082/) (2022)
- [Revisiting Heterophily For Graph Neural Networks](https://mlsanity.com/paper/2210.07606/) (2022)
- [Subgraph Federated Learning with Missing Neighbor Generation](https://mlsanity.com/paper/2106.13430/) (2021)

## Built on by (most cited first, 25 of 25)

- [The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges](https://mlsanity.com/paper/2407.09618/) (2024)
- [Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement](https://mlsanity.com/paper/2505.12684/) (2025)
- [Modeling Inter-Intra Heterogeneity for Graph Federated Learning](https://mlsanity.com/paper/2412.11402/) (2024)
- [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)
- [Rethinking Federated Graph Learning: A Data Condensation Perspective](https://mlsanity.com/paper/2505.02573/) (2025)
- [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)
- [Personalized One-shot Federated Graph Learning for Heterogeneous Clients](https://mlsanity.com/paper/2411.11304/) (2024)
- [Data-centric Federated Graph Learning with Large Language Models](https://mlsanity.com/paper/2503.19455/) (2025)
- [Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach](https://mlsanity.com/paper/2601.21369/) (2026)
- [FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space](https://mlsanity.com/paper/2608.21096/) (2026)
- [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)
- [Heterogeneity-Aware Knowledge Sharing for Graph Federated Learning](https://mlsanity.com/paper/2601.21589/) (2026)
- [Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs](https://mlsanity.com/paper/2603.29384/) (2026)
- [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)
- [FedSA-GCL: A Semi-Asynchronous Federated Graph Learning Framework with Personalized Aggregation and Cluster-Aware Broadcasting](https://mlsanity.com/paper/2507.18219/) (2025)
- [DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning](https://mlsanity.com/paper/2508.11530/) (2025)
- [BoostFGL: Boosting Fairness in Federated Graph Learning](https://mlsanity.com/paper/2601.16496/) (2026)
- [Beyond Rigid Alignment: Graph Federated Learning via Dual Manifold Calibration](https://mlsanity.com/paper/2605.06260/) (2026)
- [Generalized Category Discovery in Federated Graph Learning](https://mlsanity.com/paper/2605.08178/) (2026)
- [Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach](https://mlsanity.com/paper/2606.20382/) (2026)
- [Towards Federated Long-Tailed Graph Learning: An Energy-Guided Dual Decoupling Approach](https://mlsanity.com/paper/2606.24237/) (2026)

## Abstract (arXiv)

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semi-supervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called underlineAdaptive underlineFederated underlineGraph underlineLearning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24% and 5.57% on community split and structure Non-iid split, respectively.