# Personalized Federated Learning With Graph

arXiv: https://arxiv.org/abs/2203.00829 · HTML: https://mlsanity.com/paper/2203.00829/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 2 Mar 2022.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [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)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Think Locally, Act Globally: Federated Learning with Local and Global Representations](https://mlsanity.com/paper/2001.01523/) (2020)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Personalized Federated Learning using Hypernetworks](https://mlsanity.com/paper/2103.04628/) (2021)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [Federated Learning for Open Banking](https://mlsanity.com/paper/2108.10749/) (2021)
- [Multi-Center Federated Learning: Clients Clustering for Better Personalization](https://mlsanity.com/paper/2108.08647/) (2021)
- [FedDANE: A Federated Newton-Type Method](https://mlsanity.com/paper/2001.01920/) (2020)
- [GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://mlsanity.com/paper/2012.04187/) (2020)
- [Learning Private Neural Language Modeling with Attentive Aggregation](https://mlsanity.com/paper/1812.07108/) (2018)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [On the Convergence of Clustered Federated Learning](https://mlsanity.com/paper/2202.06187/) (2022)
- [Real-World Image Datasets for Federated Learning](https://mlsanity.com/paper/1910.11089/) (2019)
- [DAGCN: Dual Attention Graph Convolutional Networks](https://mlsanity.com/paper/1904.02278/) (2019)
- [Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health](https://mlsanity.com/paper/2108.10761/) (2021)

## Built on by (most cited first, 29 of 29)

- [Federated Learning on Non-IID Graphs via Structural Knowledge Sharing](https://mlsanity.com/paper/2211.13009/) (2022)
- [Personalized Subgraph Federated Learning](https://mlsanity.com/paper/2206.10206/) (2022)
- [Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data](https://mlsanity.com/paper/2301.09152/) (2023)
- [Towards Personalized Federated Learning via Heterogeneous Model Reassembly](https://mlsanity.com/paper/2308.08643/) (2023)
- [Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration](https://mlsanity.com/paper/2309.11103/) (2023)
- [Dual Personalization on Federated Recommendation](https://mlsanity.com/paper/2301.08143/) (2023)
- [Federated Prompt Learning for Weather Foundation Models on Devices](https://mlsanity.com/paper/2305.14244/) (2023)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [Find Your Friends: Personalized Federated Learning with the Right Collaborators](https://mlsanity.com/paper/2210.06597/) (2022)
- [Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey](https://mlsanity.com/paper/2307.04338/) (2023)
- [FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment](https://mlsanity.com/paper/2305.06124/) (2023)
- [Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs](https://mlsanity.com/paper/2306.01240/) (2023)
- [FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks](https://mlsanity.com/paper/2309.09517/) (2023)
- [A Comprehensive Data-centric Overview of Federated Graph Learning](https://mlsanity.com/paper/2507.16541/) (2025)
- [BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning](https://mlsanity.com/paper/2305.05221/) (2023)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation](https://mlsanity.com/paper/2401.15874/) (2024)
- [Sheaf HyperNetworks for Personalized Federated Learning](https://mlsanity.com/paper/2405.20882/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning](https://mlsanity.com/paper/2407.15464/) (2024)
- [FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworks](https://mlsanity.com/paper/2501.16379/) (2025)
- [Personalized Federated Learning via Learning Dynamic Graphs](https://mlsanity.com/paper/2503.05474/) (2025)
- [Graph Learning Across Data Silos](https://mlsanity.com/paper/2301.06662/) (2023)
- [FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data](https://mlsanity.com/paper/2506.20245/) (2025)
- [M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction](https://mlsanity.com/paper/2210.16193/) (2022)
- [WikiDBGraph: A Data Management Benchmark Suite for Collaborative Learning over Database Silos](https://mlsanity.com/paper/2505.16635/) (2025)
- [Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting](https://mlsanity.com/paper/2603.01363/) (2026)
- [Federated Weather Modeling on Sensor Data](https://mlsanity.com/paper/2605.00322/) (2026)
- [Geographically Regularized AUC-Maximizing Personalized Federated Learning](https://mlsanity.com/paper/2609.08379/) (2026)

## 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