mlsanityData to 7 Oct 2026

Personalized Federated Learning With Graph

arXiv 2203.00829 ↗First posted 2 Mar 2022cs.LGcited by 29

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Abstract (arXiv)

Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by aggregating models from all clients, regardless of their relation graph. This paper aims to enhance the knowledge-sharing process in PFL by leveraging the graph-based structural information among clients. We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data. We cast SFL with graph into a novel optimization problem that can model the client-wise complex relations and graph-based structural topology by a unified framework. Moreover, in addition to using an existing relation graph, SFL could be expanded to learn the hidden relations among clients. Experiments on traffic and image benchmark datasets can demonstrate the effectiveness of the proposed method. All implementation codes are available on Github

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

30 of 46 references.

References · 30

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2020Adaptive Federated Optimizationcited by 1,036
  9. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  10. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  11. 2020The Future of Digital Health with Federated Learningcited by 625
  12. 2019Federated Learning with Personalization Layerscited by 533
18 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2020Adaptive Personalized Federated Learningcited by 353
  4. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  6. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  7. 2021Personalized Federated Learning using Hypernetworkscited by 210
  8. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  9. 2021Federated Learning for Open Bankingcited by 138
  10. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  11. 2020FedDANE: A Federated Newton-Type Methodcited by 76
  12. 2020GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphscited by 64
  13. 2018Learning Private Neural Language Modeling with Attentive Aggregationcited by 49
  14. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  15. 2022On the Convergence of Clustered Federated Learningcited by 31
  16. 2019Real-World Image Datasets for Federated Learningcited by 30
  17. 2019DAGCN: Dual Attention Graph Convolutional Networkscited by 9
  18. 2021Federated Learning for Privacy-Preserving Open Innovation Future on Digital Healthcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 12; 2024: 4; 2025: 5; 2026: 3.

29 papers.

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  1. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  2. 2022Personalized Subgraph Federated Learningcited by 54
  3. 2023Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Datacited by 40
  4. 2023Towards Personalized Federated Learning via Heterogeneous Model Reassemblycited by 31
  5. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  6. 2023Dual Personalization on Federated Recommendationcited by 19
  7. 2023Federated Prompt Learning for Weather Foundation Models on Devicescited by 17
  8. 2022Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applicationscited by 14
  9. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  10. 2023Privacy-Preserving Graph Machine Learning from Data to Computation: A Surveycited by 10
  11. 2023FedDWA: Personalized Federated Learning with Dynamic Weight Adjustmentcited by 9
  12. 2023Federated Learning of Models Pre-Trained on Different Features with Consensus Graphscited by 8
17 more
  1. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  2. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  3. 2023BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learningcited by 5
  4. 2023Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networkscited by 5
  5. 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
  6. 2024Sheaf HyperNetworks for Personalized Federated Learningcited by 5
  7. 2024The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learningcited by 3
  8. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  9. 2025Personalized Federated Learning via Learning Dynamic Graphscited by 2
  10. 2023Graph Learning Across Data Siloscited by 1
  11. 2025FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworkscited by 1
  12. 2025FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Datacited by 1
  13. 2022M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data predictioncited by 0
  14. 2025WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Siloscited by 0
  15. 2026Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecastingcited by 0
  16. 2026Federated Weather Modeling on Sensor Datacited by 0
  17. 2026Geographically Regularized AUC-Maximizing Personalized Federated Learningcited by 0